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Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-25 Seong‐O Shim, Lal Hussain, Wajid Aziz, Abdulrahman A. Alshdadi, Abdulrahman Alzahrani, Abdulfattah Omar
Accurate brain tumor classification is crucial for enhancing the diagnosis, prognosis, and treatment of glioblastoma patients. We employed the ResNet101 deep learning method with transfer learning to analyze the 2021 Radiological Society of North America (RSNA) Brain Tumor challenge dataset. This dataset comprises four structural magnetic resonance imaging (MRI) sequences: fluid‐attenuated inversion‐recovery
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A systematic review of deep learning methods for the classification and segmentation of prostate cancer on magnetic resonance images Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-22 R. Deiva Nayagam, D. Selvathi
Prostate Cancer (PCa) is a prevalent global threat to male health, contributing significantly to male cancer‐related mortality. Timely detection and management are pivotal for improved outcomes, as successful cure rates are highest in the early stages. Deep learning (DL) methodologies offer a promising avenue to enhance the precision of detection, potentially reducing mortality rates. Magnetic resonance
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CAB‐Net: Convolutional attention BLSTM network for enhanced leukocyte classification Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-22 Candra Zonyfar, Jeong‐Dong Kim
In recent years, deep learning techniques have been increasingly utilized to automate and reduce human errors in leukocyte classification. However, reliability and accuracy issues remain a challenge. This paper proposes CAB‐Net, an end‐to‐end deep learning‐based model that integrates a convolutional neural network with an attention mechanism and BLSTM. The model uses a stack of convolutional and pooling
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Computerized diagnosis of knee osteoarthritis from x‐ray images using combined texture features: Data from the osteoarthritis initiative Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-22 Khadidja Messaoudene, Khaled Harrar
The prevalence of knee osteoarthritis (KOA) cases has witnessed a significant increase on a global scale in recent years, emphasizing the need for automated diagnostic computer systems to aid in early‐stage osteoarthritis (OA) diagnosis. The accurate characterization of knee KOA stages through feature extraction poses significant research challenges due to the complexity of identifying relevant attributes
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Skin lesion classification based on hybrid self‐supervised pretext task Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-20 Dedong Yang, Jianwen Zhang, Yangyang Li, Zhiquan Ling
The combination of observation of skin lesion and digital image technology contributes to the diagnosis and treatment of skin diseases. To solve the problems of large variation of target size and shape in skin disease images, small difference between disease images and normal images, and difficulty of label acquisition, we propose a classification algorithm for skin lesion based on hybrid self‐supervised
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RR‐HCL‐SVM: A two‐stage framework for assessing remaining thyroid tissue post‐thyroidectomy in SPECT images Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-20 Minh Lai Phu, Thanh Vinh Pham, Thuc Pham Duc, Trung Nguyen Thanh, Long Tran Quoc, Duc Chu Minh, Ha Le Ngoc, Son Mai Hong, Phuong Nguyen Thi, Nhung Nguyen Thi, Khanh Le Quoc, Thuan Duc Nguyen, Ha Nguyen Thai, Thanh Nguyen Chi
This paper presents a two‐stage deep learning framework, RR‐HCL‐SVM, designed to aid in the assessment of residual thyroid tissues following thyroidectomy, utilizing single‐photon emission computed tomography (SPECT) images. Leveraging the power of deep learning, our model offers a comprehensive solution for the detection and assessment of remaining thyroid tissues. To enhance accuracy, we introduce
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Automatic detection of knee osteoarthritis grading using artificial intelligence‐based methods Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-19 Muhammed Yildirim, Hurşit Burak Mutlu
Osteoarthritis (OA) means that the slippery cartilage tissue that covers the bone surfaces in the joints and allows the joint to move easily loses its properties and wears out. Knee OA is the wear and tear of the cartilage in the knee joint. Knee OA is a disease whose incidence increases especially after a certain age. Knee OA is difficult and costly to be detected by specialists using traditional
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Identification of biological markers in cancer disease using explainable artificial intelligence Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-19 Muhammad Shahzad, Ruhal Lohana, Khursheed Aurangzeb, Isbah Imtiaz Ali, Muhammad Shahid Anwar, Mahnoor Murtaza, Rauf Ahmed Shams Malick, Piratdin Allayarov
The research aims to improve the prediction of drug sensitivity on cancer cell lines using gene expression data and molecular fingerprints of drugs. The proposed study uses a deep learning model, BioMarkerX, trained on the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) datasets utilizing Particle Swarm Optimization technique to select specific genes as features
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RIBM3DU‐Net: Glioma tumour substructures segmentation in magnetic resonance images using residual‐inception block with modified 3D U‐Net architecture Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-18 Syedsafi Shajahan, Sriramakrishnan Pathmanaban, Kalaiselvi Tiruvenkadam
Glioma brain tumour is one of the life‐threatening diseases in the world. Tumour substructure segmentation by physicians is a time‐consuming task with the magnetic resonance imaging (MRI) technique due to the size of clinical data. An automatic and well‐trained method is essential to detect and segment the tumour which increase the survival of the patients. The proposed work aims to produce high accuracy
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Brain tumor image segmentation algorithm based on multimodal feature fusion of Bayesian weight distribution Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-16 Ju Li, Yanhui Wang, Guoliang Wang
This study proposes an improved U‐Net model to address the issues of large semantic differences in skip connections and insufficient utilization of cross‐channel information in magnetic resonance imaging (MRI) images leading to inaccurate segmentation of brain tumor regions in the field of brain tumor segmentation. Firstly, by adding a deep residual module to alter the receptive field, the network's
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Cover Image Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-14 Jindong Wu, Qunzheng Mi, Yi Zhang, Tongning Wu
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A joint autoencoder and classifier deep neural network for AD and MCI classification Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-08 Reema Ganotra, Shailender Gupta, Shirin Dora
In this article, we present a new approach to distinguish progressive mild cognitively impaired (pMCI) subjects, who eventually develop Alzheimer's disease (AD) from stable MCI (sMCI) subjects whose situation does not deteriorate into AD. The proposed approach combines the discriminating capabilities of classifiers and representation learning capacities of autoencoders into a unified architecture,
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Retraction: “Implementation of deep neural networks for classifying electroencephalogram signal using fractional S‐transform for epileptic seizure detection” Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-07
Retraction: Ashokkumar SR, Anupallavi S, Premkumar M, Jeevanantham V. Implementation of deep neural networks for classifying electroencephalogram signal using fractional S‐transform for epileptic seizure detection. Int J Imaging Syst Technol. 2021;31:895–908. https://doi.org/10.1002/ima.22565The above article, published online on 02 March 2021 in Wiley Online Library (wileyonlinelibrary.com), has been
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Improved senescent cell segmentation on bright‐field microscopy images exploiting representation level contrastive learning Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-06 Fatma Çelebi, Dudu Boyvat, Serife Ayaz‐Guner, Kasim Tasdemir, Kutay Icoz
Mesenchymal stem cells (MSCs) are stromal cells which have multi‐lineage differentiation and self‐renewal potentials. Accurate estimation of total number of senescent cells in MSCs is crucial for clinical applications. Traditional manual cell counting using an optical bright‐field microscope is time‐consuming and needs an expert operator. In this study, the senescence cells were segmented and counted
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D‐Unet: A symmetric architecture of convolutional neural network with two auxiliary outputs for dementia recognition Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-04 Siying Li, Pan Xia, Yonggang Zou, Lidong Du, Zhenfeng Li, Peng Wang, Xianxiang Chen, Yundai Chen, Yajun Shi, Zhen Fang
Dementia‐associated disorders cause damage to the brains of patients and bring huge burdens to individuals and families. Electroencephalogram (EEG) monitoring is friendly to patients on account of low cost, non‐invasion, and objective. Event‐related potential (ERP) is a component of EEG that has huge potential to evaluate the cognitive function of the brain. In this study, we recorded the ERP from
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An adaptive weight search method based on the Grey wolf optimizer algorithm for skin lesion ensemble classification Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-04 Luzhou Liu, Xiaoxia Zhang, Zhinan Xu
Skin cancer is a common type of malignant tumor that poses a serious threat to patients' lives and health, especially melanoma. It may spread to other body parts, resulting in serious complications and death. In the medical field, accurate identification of skin lesion images is crucial for diagnosing different diseases. However, due to the similarity between different skin lesions, it brings some
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Advancing skin cancer diagnosis with a multi‐branch ShuffleNet architecture Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-04 G. Prince Devaraj, R. Ravi
In this study, we present an innovative approach for enhancing skin cancer classification through a multi‐branch architecture inspired by ShuffleNet. Our methodology focuses on improving feature extraction and representation, emphasizing cross‐channel information exchange to achieve superior accuracy. The architecture comprises three branches: a primary feature enhancement branch, a parallel feature
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Constructing brain functional networks with adaptive manifold regularization for early mild cognitive impairment Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-04 Xidong Fu, Shengchang Shan, Chun Liu, Yu Lu, Zhuqing Jiao
Brain functional network (BFN) has emerged as a practical path to explore biomarkers for early mild cognitive impairment (eMCI). Currently, most of BFNs only considered the topology structure between two brain regions and ignored the high‐order information among multiple brain regions. We proposed an adaptive manifold regularization method to construct a new BFN. Firstly, a traditional hypergraph was
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Assessment of magnetic resonance imaging and histopathological changes in chordoid glioma: A retrospective analysis of 13 cases Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-02 Cong Huang, Li‐hua Deng, Feng Wen, Peng He, Xing‐shun Zhou, Zhen‐ni Yu, Zi‐lin Zhao, Jun‐de Luo
Chordoid glioma (CG) is a rare tumor, and its predominantly anterior location is in the third ventricle. However, the clinical characteristics of CG were not obvious, and there was less knowledge of CG‐related imaging. This study aimed at exploring the MRI imaging features of CG. We retrospectively analyzed the MRI data of 13 patients diagnosed with CG from January 2015 to May 2021. The pathological
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CerviFormer: A pap smear‐based cervical cancer classification method using cross‐attention and latent transformer Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-02-27 Bhaswati Singha Deo, Mayukha Pal, Prasanta K. Panigrahi, Asima Pradhan
Cervical cancer is one of the primary causes of death in women. It should be diagnosed early and treated according to the best medical advice, similar to other diseases, to ensure that its effects are as minimal as possible. Pap smear images are one of the most constructive ways for identifying this type of cancer. This study proposes a cross‐attention‐based Transfomer approach for the reliable classification
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Deep hybrid model for Mpox disease diagnosis from skin lesion images Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-02-27 Saif Ur Rehman Khan, Sohaib Asif, Omair Bilal, Sajid Ali
This research presents DNLR‐NET, a novel model designed for automated and accurate diagnosis of MPox disease. The model's performance is constructed and validated using a carefully collected MPox dataset from online repositories. DNLR‐NET begins by extracting deep features from the DenseNet201 pre‐trained model, which exhibited superior performance compared to other models during the comparison. The
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A combination of multi‐scale and attention based on the U‐shaped network for retinal vessel segmentation Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-02-27 Yan Zhang, Qingyan Lan, Yemei Sun, Chunming Ma
Automated partitioning of retinal vessels depicted in fundus images is beneficial in the detection of specific ailments like hypertension and diabetes. However, retinal vessel images have the problems of a large semantic range, more spatial detail, and limited differentiation among the blood vessels and surroundings, which make vessel segmentation challenging. To overcome these obstacles, we designed
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Efficient spine segmentation network based on multi‐scale feature extraction and multi‐dimensional spatial attention Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-02-27 Guohao Xu, Chuantao Wang, Zhuoyuan Li, Jiliang Zhai, Saishuo Wang
In spine imaging, efficient automatic segmentation is crucial for clinical decision‐making, yet current models increase accuracy at the expense of elevated parameter counts and computational complexity, complicating integration with contemporary medical devices. Addressing identified challenges, this research introduces LE‐NeXt, a spine segmentation framework utilizing multi‐dimensional spatial attention
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A customized ConvNeXt-XL network with fusion of deep and handcrafted features for colposcopy image classification Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-02-19 Thendral Natarajan, Lakshmi Devan, Ramaprabha Palayanoor Seethapathy, Senthil Kumar Balakrishnan
Cervical cancer is the second most frequent cancer among women of all age groups worldwide. It occurs due to human papillomavirus. In the premature stages, the symptoms will not be predominant until they reach the final stage of cancer. Detection and classification of cervical cancer always demand gynecologists with the necessary skills and experience. The goal of the proposed work is to develop a
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Dual independent pathway‐densely connected residual network with dilated convolution‐based arterial spin labeling MRI image reconstruction with minimum label‐control pairs Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-02-21 A. Shyna, C. Ushadevi Amma, Ansamma John, C. Kesavadas, Bejoy Thomas
Arterial spin labeling (ASL) is a non‐invasive MRI technique widely used to measure cerebral blood flow (CBF), but it suffers from poor SNR, requiring the acquisition of a large number of multiple Label‐Control (L‐C) pairs at the expense of prolonged acquisition time. This work proposed a novel deep learning network to reconstruct the ASL CBF map using the minimum number of L‐C pairs. A dataset comprising
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Identifying and matching 12‐level multistained glomeruli via deep learning for diagnosis of glomerular diseases Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-02-21 Qiming He, Siqi Zeng, Shuang Ge, Yanxia Wang, Jing Ye, Yonghong He, Tian Guan, Zhe Wang, Jing Li
The assessment of glomerular lesions is a fundamental step toward the diagnosis of glomerular diseases. This requires diagnosis and fusion of information from all the glomeruli at multiple levels and stainings. The lack of research on multi‐level multistained glomerular identification and matching has resulted in renal pathologists devoting much time and attention to this time‐consuming and labor‐intensive
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Karyotyping of human chromosomes in metaphase images using faster R‐CNN and inception models Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-02-21 Satishkumar Chavan, Leeaa Nair, Nishant Nimbalkar, Sarah Solkar
Karyotyping is the process of pairing and ordering human chromosomes from metaphase chromosomal images depending on their size, centromere position, and banding patterns. It is used to analyze human chromosomes for various genetic disorders especially during prenatal screenings. Since manual karyotyping is a labor‐intensive and a time‐consuming task, developing an automatic or semi‐automatic computer‐assisted
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Expression of Concern: A gingivitis identification method based on contrast-limited adaptive histogram equalization, gray-level co-occurrence matrix, and extreme learning machine Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-02-13
Li W, Chen Y, Sun W, Brown, M, Zhang, X, Wang, S, and Miao, L. A gingivitis identification method based on contrast-limited adaptive histogram equalization, gray-level co-occurrence matrix, and extreme learning machine. Int J Imaging Syst Technol. 2019;29:77–82. https://doi.org/10.1002/ima.22298 This Expression of Concern is for the above article, published online on November 17, 2018, in Wiley Online
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Unmasking pancreatic cancer: Advanced biomedical imaging for its detection in native versus arterial dual-energy computed tomography (DECT) scans Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-02-14 Jennifer Gotta, Leon D. Gruenewald, Simon S. Martin, Christian Booz, Katrin Eichler, Scherwin Mahmoudi, Canan Özdemir Rezazadeh, Philipp Reschke, Teodora Biciusca, Lisa-Joy Juergens, Christoph Mader, Renate Hammerstingl, Christof M. Sommer, Thomas J. Vogl, Vitali Koch
This study investigates the potential of a machine learning classifier using dual- energy computed tomography (DECT) radiomics to differentiate between malignant pancreatic lesions and normal pancreas tissue. A total of 100 patients who underwent third-generation DECT between November 2018 and October 2022 were included, with 60 patients having pancreatic cancer and 40 normal pancreatic tissue. Radiomics
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A target region extraction method for ultrasound medical images based on improved PRIDNet and UCTransNet Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-02-09 Jintao Zhai, Feng Tian, Ang Li, Shengyou Qian, Runmin Wang, Xiao Zou
Computer-aided diagnosis is pivotal in augmenting the diagnostic efficiency of ultrasound images. Nonetheless, the substantial presence of noise and artifacts in ultrasound images presents a challenge to the precise segmentation of the target region. To highlight and analyze the diagnostic information such as tissues, organs, and lesions in ultrasound medical images more accurately, a target region
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Retraction: Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-02-05
Liu T, Yuan Z, Wu L, Badami B. Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm. Int J Imaging Syst Technol. 2021;31:1921–1935. https://doi.org/10.1002/ima.22559
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Evaluation of optimal interpolation and segmentation of the optic nerves on magnetic resonance images for cross-sectional area measurement Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-30 Li Sze Chow, Martyn N. J. Paley, Simon J. Hickman
This study investigated nine combination methods produced from three interpolation (Lanczos, iterative curvature based interpolation, and contrast-guided) and three segmentation (spatial fuzzy C-means, modified fuzzy C-means [mFCM], and level set method) models from optic nerve magnetic resonance images (MRI). The aim was to produce sharp edges of the optic nerves for cross-sectional area measurement
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Differentiation of COVID-19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-02-01 Isaac Shiri, Yazdan Salimi, Abdollah Saberi, Masoumeh Pakbin, Ghasem Hajianfar, Atlas Haddadi Avval, Amirhossein Sanaat, Azadeh Akhavanallaf, Shayan Mostafaei, Zahra Mansouri, Dariush Askari, Mohammadreza Ghasemian, Ehsan Sharifipour, Saleh Sandoughdaran, Ahmad Sohrabi, Elham Sadati, Somayeh Livani, Pooya Iranpour, Shahriar Kolahi, Bardia Khosravi, Maziar Khateri, Salar Bijari, Mohammad Reza Atashzar
To derive and validate an effective machine learning and radiomics-based model to differentiate COVID-19 pneumonia from other lung diseases using a large multi-centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID-19; 9657 other lung diseases including non-COVID-19 pneumonia, lung cancer, pulmonary
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CCheXR-Attention: Clinical concept extraction and chest x-ray reports classification using modified Mogrifier and bidirectional LSTM with multihead attention Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-28 Somiya Rani, Amita Jain, Akshi Kumar, Guang Yang
Radiology reports cover different aspects from radiological observation to the diagnosis of an imaging examination, such as x-rays, magnetic resonance imaging, and computed tomography scans. Abundant patient information presented in radiology reports poses a few major challenges. First, radiology reports follow a free-text reporting format, which causes the loss of a large amount of information in
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Diagnosis of glaucoma from retinal fundus images using disc localization and sequential DNN model Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-28 Kamesh Sonti, Ravindra Dhuli
Deep learning is an emerging trend with enormous applications over the past few years. Ophthalmology is one such area in medical applications where early disease detection is required to avoid loss of vision. Glaucoma is a rapidly growing disorder related to human eye, which arises due to the increase in pressure inside the eye. The medical diagnosis methods available for glaucoma have some limitations;
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Automatic measurement of fetal abdomen subcutaneous soft tissue thickness from ultrasound image based on a U-shaped attention network with morphological method Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-28 Zhenming Yuan, Tianhao Xu, Cheng Yu, Xiaojun Ye, Jian Zhang
Fetal abdominal subcutaneous soft tissue thickness (FASSTT) is a key indicator in evaluating fetal growth, development, and nutritional status. Currently, manual measurement in FASSTT faces the problems of difficulty in positioning, time consumption, and inaccurate measurement. Therefore, this article proposes an automatic measurement scheme for FASSTT. Firstly, a U-shaped attention network VGG-SeUnet
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Two-dimensional medical image segmentation based on U-shaped structure Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-25 Sijing Cai, Yuwei Xiao, Yanyu Wang
With rapid developments in convolutional neural networks for image processing, deep learning methods based on pixel classification have been extensively applied in medical image segmentation. One popular strategy for such tasks is the encoder-decoder-based U-Net architecture and its variants. Most segmentation methods based on fully convolutional networks will cause the loss of spatial and contextual
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Development of handheld optical coherence tomography based on commercial intra-oral scanner shape for extended clinical utility in dentistry Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-23 Hayoung Kim, Hoseong Cho, Weonjoon Lee, Keunbada Son, Kyubok Lee, Mansik Jeon, Jeehyun Kim
The main objective of this study is to develop a handheld dental optical coherence tomography (OCT) system capable of imaging the target area of the teeth in the oral cavity and demonstrate the applicability of the developed system to in vivo dental disease diagnosis based on a user-friendly scanner form. The design of the developed system is based on mimicking the shape of a commonly used commercial
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An improved semantic segmentation for breast lesion from dynamic contrast enhanced MRI images using deep learning Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-19 C. Sahaya Pushpa Sarmila Star, A. Milton, T. M. Inbamalar
The World Health Organization (WHO) reports that approximately 2.3 million breast cancer cases are diagnosed each year. Early detection is key to tackling this issue, and Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is a preferred method for detecting tumors. Convolutional Neural Networks (CNNs) can accurately segment images without human assistance. The objective of this study is
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CGS-Net: A classification-guided framework for automated infection segmentation of COVID-19 from CT images Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-12 Wen Zhou, Jihong Wang, Yuhang Wang, Zijie Liu, Chen Yang
Automated segmentation of lung lesions in CT images of COVID-19 based on deep learning holds great potential for comprehending the advancement of the disease and establishing suitable treatment approaches. However, the complex background, indistinct boundaries, varying sizes and distributions of infected regions, and high similarity to other lung diseases pose substantial challenges. To address these
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End-to-end light-weighted deep-learning model for abnormality classification in kidney CT images Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-12 V. Karthikeyan, M. Navin Kishore, S. Sajin
Kidney disease is a major health problem that affects millions of people around the world. Human kidney problems can be diagnosed with the help of computed tomography (CT), which creates cross-sectional slices of the organ. A deep end-to-end convolutional neural network (CNN) model is proposed to help radiologists detect and characterize kidney problems in CT scans of patients. This has the potential
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In silico, in vitro, and in vivo validation of a microwave imaging system using a low-profile Ultra Wide Band Archimedean spiral antenna to detect skin cancer Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-08 Komalpreet Kaur, Amanpreet Kaur
Microwave imaging (MI) is a noninvasive and nonionizing procedure for detection of cancerous cells in healthy body tissues using radiofrequency (RF) and microwaves. The procedure involves the use of Ultra Wide Band (UWB) antennas for sensing purposes. Therefore, this research article presents the design, development, and testing of a low-profile UWB Archimedean spiral microstrip-patch antenna (ASMA)
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A comprehensive study on automatic non-informative frame detection in colonoscopy videos Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-08 Rukiye Nur Kaçmaz, Refika Sultan Doğan, Bülent Yılmaz
Despite today's developing healthcare technology, conventional colonoscopy is still a gold-standard method to detect colon abnormalities. Due to the folded structure of the intestine and visual disturbances caused by artifacts, it can be hard for specialists to detect abnormalities during the procedure. Frames that include artifacts such as specular reflection, improper contrast levels from insufficient
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A brain tumour classification on the magnetic resonance images using convolutional neural network based privacy-preserving federated learning Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-08 Şevket Ay, Ekin Ekinci, Zeynep Garip
The healthcare industry has found it challenging to build a powerful global classification model due to the scarcity and diversity of medical data. The leading cause is privacy, which restricts data sharing among healthcare providers. Federated learning (FL) can contribute to developing classification models by protecting data privacy. This study has tested various federated techniques in a peer-to-peer
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Correction to “Automated detection and classification of skin diseases using diverse features and improved gray wolf-based multiple-layer perceptron neural network” Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-08
Melbin K, Jacob Vetha Raj Y. Automated detection and classification of skin diseases using diverse features and improved gray wolf-based multiple-layer perceptron neural network. Int J Imaging Syst Technol. 2021;31:1317–1333. doi:10.1002/ima.22524 In the article cited above, the university name was omitted for the affiliation of both authors. The full affiliation should read: Department of Computer
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Wavelet scattering- and object detection-based computer vision for identifying dengue from peripheral blood microscopy Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-08 Liora Rosvin Dsilva, Shivani Harish Tantri, Niranjana Sampathila, Hilda Mayrose, G. Muralidhar Bairy, Sushma Belurkar, Kavitha Saravu, Krishnaraj Chadaga, Abdul Hafeez-Baig
Dengue fever infection is a global health concern. Early disease detection is crucial for averting complications and fatality. Characteristic morphological changes in lymphocytes can be observed on a peripheral blood smear (PBS) in cases of dengue infection. In this research, we have developed automated computer vision models for dengue detection on PBS images using two approaches: wavelet scattering
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Quantum-inspired hybrid algorithm for image classification and segmentation: Q-Means++ max-cut method Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-01-08 Suman Kumar Roy, Bhawana Rudra
Finding brain tumors is a crucial step in medical diagnosis that can have a big impact on how patients turn out. Conventional detection techniques can be laborious and demand a lot of computing power. Brain tumor detection could be made more effective and precise, thanks to the quickly developing field of quantum computing. In this article, we propose a quantum machine learning (QML)-based method for
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An efficient lung image classification and detection using spiral-optimized Gabor filter with convolutional neural network Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2023-12-29 V. Sivakumar, C. K. Yogesh, S. Vatchala, S. Kaliraj
Lung cancer has a high death rate of around seven million cases every year worldwide. A computed tomography (CT) scan provides certain essential data concerning lung diseases and their diagnosis. The main objective of this work is to classify various lung diseases such as Normal, Bronchiectasis, and Pleural Effusion. The proposed approach consists of three stages, namely pre-processing, feature extraction
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An efficient wavelet thresholding strategy and robust shrinkage approach for de-noising ECG signal Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2023-12-28 Anas Fouad Ahmed
This paper introduces an efficient wavelet thresholding strategy and robust shrinkage approach (WTS) for de-noising the ECG signals. The optimal mother wavelet for de-noising the ECG signal is automatically selected based on cross-correlation and energy-to-entropy indices. A dynamic threshold is applied to various levels of decomposition to eliminate different types of noise. A shrinkage approach is
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Deep learning-based survival prediction of brain tumor patients using attention-guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2023-12-22 Moona Mazher, Abdul Qayyum, Domenec Puig, Mohamed Abdel-Nasser
Automatic survival prediction of gliomas from brain magnetic resonance imaging (MRI) volumes is an essential step for a patient's prognosis analysis. Radiomics research delivers beneficial feature information from MRI imaging which is substantially required by clinicians and oncologists for predicting disease prognosis for precise surgical treatment and planning. In recent years, the success of deep
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Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2023-12-22 Naeem Ullah, Muhammad Hassan, Javed Ali Khan, Muhammad Shahid Anwar, Khursheed Aurangzeb
Early detection of brain tumors is vital for improving patient survival rates, yet the manual analysis of the extensive 3D MRI images can be error-prone and time-consuming. This study introduces the Deep Explainable Brain Tumor Deep Network (DeepEBTDNet), a novel deep learning model for binary classification of brain MRIs as tumorous or normal. Employing sub-image dualistic histogram equalization (DSIHE)
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A deep learning-based x-ray imaging diagnosis system for classification of tuberculosis, COVID-19, and pneumonia traits using evolutionary algorithm Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2023-12-21 Zeeshan Ali, Muhammad Attique Khan, Ameer Hamza, Ahmed Ibrahim Alzahrani, Nasser Alalwan, Mohammad Shabaz, Faheem Khan
To aid in detection of tuberculosis, researchers have concentrated on developing computer-aided diagnostic technologies based on x-ray imaging. Since it generates noninvasive standard-of-care data, a chest x-ray image is one of the most often used diagnostic imaging modalities in computer-aided solutions. Due to their significant interclass similarities and low intra-class variation abnormalities,
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BCUIS-Net: A breast cancer ultrasound image segmentation network via boundary-aware and shape feature fusion Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2023-12-20 Haiyan Li, Xu Wang, Yiyin Tang, Shuhua Ye
Breast cancer is a highly lethal disease with the highest mortality rate among women worldwide. Breast tumor segmentation from ultrasound images plays a critical role in enabling early detection, leading to a reduction in mortality rates. However, the challenge of ultrasound breast cancer segmentation arises from factors such as indistinct lesion boundaries, noise artifacts, and inhomogeneous intensity
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Brain tumor grade classification using multi-step pre-training Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2023-12-16 Yasar Mehmood, Usama Ijaz Bajwa, Muhammad Waqas Anwar
Medical images offer a non-invasive method to diagnose different diseases, but using them manually produces unreliable results. Modern deep learning architectures and techniques are computed and data-intensive, making them difficult to use for relatively smaller datasets of medical images. Transfer learning has been used as a remedy for the problem mentioned above. However, the domain difference between
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Deep learning approach for brain tumor classification using metaheuristic optimization with gene expression data Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2023-12-16 Amol Avinash Joshi, Rabia Musheer Aziz
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Few-shot segmentation for esophageal OCT images based on self-supervised vision transformer Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2023-12-16 Cong Wang, Meng Gan
Automatic segmentation of layered tissue is the key to optical coherence tomography (OCT) image analysis for esophagus. While deep learning technology offers promising solutions to this problem, the requirement for large numbers of annotated samples often poses a significant obstacle, as it is both expensive and challenging to obtain. With this in mind, we introduced a self-supervised segmentation
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An efficient convolutional histogram-oriented gradients and deep convolutional learning approach for accurate classification of bone cancer Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2023-12-12 J. Vijayaraj, B. Abirami, Sachi Nandan Mohanty, V. P. Kavitha
In our human body bones are the most significant part, which helps people to move and perform several activities. But, the cancer is caused by producing abnormal cell, which is rapidly spread to the whole parts of the body. Bone cancer is one of the critical types due to its malignancy more than other cancers. The approach involves preprocessing and segmentation of input images to remove noise and
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Dual-branch feature extraction network combined with Transformer and CNN for polyp segmentation Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2023-12-11 Qiaohong Liu, Yuanjie Lin, Xiaoxiang Han, Keyan Chen, Weikun Zhang, Hui Yang
To overcome the difficulty of accurate polyp segmentation, a novel encoder–decoder network DFETC-Net is proposed, in which two encoders based on Swin Transformer and CNN are utilized to extract the global and local features respectively. Further, a new self-attention and convolution feature fusion module is designed to fuse the two branch features to enhance the feature representative capability and
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CVINet: A deep learning based model for the diagnosis of chronic venous insufficiency in lower extremity using infrared thermal images Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2023-12-09 Nithyakalyani Krishnan, P. Muthu
Chronic venous insufficiency (CVI) is a venous disorder characterized by impaired blood flow from the lower extremities back to the heart, leading to various symptoms and complications. Accurate and timely diagnosis of CVI is critical for effective management and prevention of further complications. Deep learning (DL) models have shown results that are promising in the field of medical image analysis
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Deep learning based Glaucoma Network Classification (GNC) using retinal images Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2023-12-08 Iqra Ashraf Kiyani, Tehmina Shehryar, Samina Khalid, Uzma Jamil, Adeel Muzaffar Syed
The proposed deep learning framework for glaucoma classification addresses critical challenges of limited data and computational costs. Employing data augmentation and normalization techniques, the three-stage model, utilizing InceptionV3 and ResNet50, achieves high training (99.3% - 99.8%) and testing accuracy (91.6% - 92.12%) on a dataset comprising 16,328 images from fused public datasets. This