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RETRACTION: Novel computer‐aided lung cancer detection based on convolutional neural network‐based and feature‐based classifiers using metaheuristics Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-04-29
RETRACTION: Z. Guo, L. Xu, Y. Si, and N. Razmjooy, “Novel computer‐aided lung cancer detection based on convolutional neural network‐based and feature‐based classifiers using metaheuristics,” International Journal of Imaging Systems and Technology 31, no. 4 (2021): 1954–1969. https://doi.org/10.1002/ima.22608The above article, published online on 5 June 2021 in Wiley Online Library, has been retracted
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A universal medically assisted model for anatomical landmark detection in radioactive images Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-04-25 Hong Pang, Shenghui Liao, Shu Liu, Xiaoyan Kui
Anatomical landmark detection is a critical task in medical image analysis with significant research and practical value. The analysis of landmarks in radiological images is beneficial for its diagnosis and treatment. Currently, most methods are applied to datasets from specific anatomical regions, with only a few deep neural network models designed for mixed datasets. Meanwhile, the precise and fast
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Predictive value of computed tomography radiomics combined with traditional imaging features in WHO/ISUP grading of clear cell renal carcinoma Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-04-23 Yu‐Ning Feng, Yu‐Hui Zhu, Xiao‐Rong Feng, Ju‐Fang Wu, Di Wei, Guang‐Di Huang, Yun‐Dan Jiang
The aim of the study is to investigate the preoperative prediction value of computed tomography (CT) radiomics combined with traditional imaging features in the grading of clear cell renal cell carcinoma (CCRCC) by extracting and analyzing the CT radiomics information of patients with CCRCC. One hundred thirty four patients with CCRCC who were admitted to our Hospital, Sun Yat‐sen University (Futian
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Gray level fuzzy deep neural networks for enhancing performance in lung disease detection: A comparative study with fuzzy logic methods Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-04-17 B. Muthukumar, B. V. V. Siva Prasad, Yeligeti Raju, Hitendra Kumar Lautre
Lung cancer is a deadly disease, and its early detection is crucial for effective treatment. In this context, accurate classification of lung cancers from computed tomography imaging is a vital research area. Irregularly detected gray matter in these images can affect classification outcomes, making accurate lung cancer detection difficult. To address this issue, researchers have developed a new approach
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CG‐Net: A novel CNN framework for gastrointestinal tract diseases classification Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-04-17 Samra Siddiqui, Tallha Akram, Imran Ashraf, Muddassar Raza, Muhammad Attique Khan, Robertas Damaševičius
The classification of medical images has had a significant influence on the diagnostic techniques and therapeutic interventions. Conventional disease diagnosis procedures require a substantial amount of time and effort to accurately diagnose. Based on global statistics, gastrointestinal cancer has been recognized as a major contributor to cancer‐related deaths. The complexities involved in resolving
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DiffSwinTr: A diffusion model using 3D Swin Transformer for brain tumor segmentation Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-04-15 Junan Zhu, Hongxin Zhu, Zhaohong Jia, Ping Ma
Automatic medical image segmentation has shown great potential in recent years. Howerver, magnetic resonance images (MRI) usually have the characteristics of noise and artifacts, existing methods cannot accurately segment the boundaries. In addition, most existing algorithms are unable to effectively capture the global dependencies to offset the local inductive bias. In this work, we present a novel
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Accurate diagnosis of dementia and Alzheimer's with deep network approach based on multi‐channel feature extraction and selection Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-04-11 Mehmet Emre Sertkaya, Burhan Ergen, Muammer Türkoğlu, Özgür Tonkal
In this article, we have proposed a multi‐stage in‐depth approach based on the improved VGGNet architecture for automatically and accurately diagnosing dementia and Alzheimer's disease. In this approach, first of all, the learned weights of the VGG16 architecture are frozen, and multichannel attributes are extracted from each pooling layer. Then, these attributes were given to the inputs of the attribute
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EENet: Application of convolutional neural network‐based deep learning methods in bone tumor pathological diagnosis Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-04-10 Xiuyan Li, Ruotong Ding, Qi Wang, Zhenyu Yang, Xiaojie Duan, Yukuan Sun, Aidong Liu
Bone tumors are one of the most common diseases in bone and soft tissue tumors, and accurate classification is crucial for developing effective treatment strategies. However, traditional pathological morphology diagnosis is subjective and uncertain, requiring highly specialized knowledge and experience. Therefore, how to efficiently and accurately classify bone tumor types based on pathological images
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An extensive upgrading of contact diffuse CorrelationTomography system Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-04-05 Xiaojuan Zhang, Ting Ding, Yu Shang, Zhiguo Gui
Diffuse correlation tomography (DCT) is an emerging tissue blood flow index (BFI) imaging technique that typically requires a large number of source‐detector pairs, resulting in high instrumentation costs. We developed a low‐cost paradigm for upgrading the DCT system with time‐sharing hardware sensors via optical switches, wherein the S‐D configuration was spatially optimized and combined with a novelty
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Lung computed tomography image enhancement using U‐Net segmentation Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-04-04 Alaa H. Sheer, Hana H. Kareem, Hazim G. Daway
The goal of image enhancement methods is to improve image's quality. The efficacy of U‐net is evident through its extensive utilization across various significant image modalities, involving computed tomography (CT) scans, magnetic resonance imaging, X‐rays, and microscopy. In this study, we provided a novel and efficient strategy to improve lung CT images based on segmentation using U‐Net architecture
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Intelligent MRI diagnosis of neurological alterations in infants from 4 to 12 months Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-04-04 Salvador Calderon‐Uribe, Luis A. Morales‐Hernandez, Jose O. De Leo‐Jimenez, Emmanuel Resendiz‐Ochoa, Manuel Toledano‐Ayala, Irving A. Cruz‐Albarran
Magnetic resonance imaging is an essential tool for the identification of neurological problems since it provides relevant information on brain development. The aim of the present work was the detection of neurological alterations in newborns from 4 to 12 months of age by segmentation and analysis of lateral ventricles in magnetic resonance images. For this purpose, an automated deep approach based
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Segmentation of the left atrium and proximal pulmonary veins based on dimensional decomposition attention Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-04-02 Guodong Zhang, Tingyu Liang, Yanlin Li, Kaichao Liang, Zhaoxuan Gong, Wei Guo, Zhuoning Zhang, Ronghui Ju
Pulmonary vein anatomical structure typing plays a crucial role in the preoperative assessment and postoperative evaluation of lung tumor resection, atrial fibrillation radio frequency ablation, and other medical procedures. The accuracy of such typing relies heavily on the segmentation results of the left atrium and proximal pulmonary veins. However, due to the similarities in intensity between the
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A hybrid convolutional and transformer network for segmentation of coronary computed tomography angiographic slices Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-04-01 Xiaojie Duan, Yanchao Wang, Jianming Wang
Coronary artery three‐dimensional reconstruction is essential for preventing, diagnosing, and treating coronary heart disease. This study introduces SegUnet, a lightweight hybrid CNN‐Transformer network for pixel‐level segmentation of coronary artery computed tomography angiography (CTA) slices to enhance the precision of coronary artery reconstruction. The overall SegUnet adopts a U‐shaped encoder‐decoder
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Efficient feature extraction and hybrid deep learning for early identification of uterine fibroids in ultrasound images Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-30 Meena Lekshmanan Chinna, Joe Prathap Pathrose Mary
Non‐cancerous growths called uterine fibroids develop in the uterus. They can vary in size, location, and number, and can produce symptoms including excessive menstrual flow, pelvic discomfort, and reproductive problems. Early detection of uterine fibroids is important because it allows for timely intervention and appropriate management strategies. Extracting meaningful features from ultrasound (US)
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An efficient brain tumor segmentation model based on group normalization and 3D U‐Net Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-30 Runlin Chen, Yangping Lin, Yanming Ren, Hao Deng, Wenyao Cui, Wenjie Liu
Accurate segmentation of brain tumors has a vital impact on clinical diagnosis and treatment, and good segmentation results are helpful for the treatment of this disease, which is a serious threat to human health. High‐precision segmentation of brain tumors remains a challenging task due to their diverse shapes, sizes, locations, and complex boundaries. Considering the special structure of medical
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Comparative analysis of cone‐beam breast computed tomography and digital breast tomosynthesis for breast cancer diagnosis: A comprehensive study on reconstruction algorithms Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-30 Temitope Emmanuel Komolafe, Yuchi Tian, Olanrewaju James Awoniya, Shuang‐Qing Chen, Xiaodong Yang
Breast cancer (BC) is the most commonly diagnosed non‐skin cancer in women. To achieve early and accurate diagnosis, three‐dimensional (3D) cone‐beam breast computed tomography (CBBCT) and digital breast tomosynthesis (DBT) modalities are used. Importantly, the comparison of reconstruction accuracy of both CBBCT and DBT has rarely been investigated, thus constituting a research gap. This study systematically
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Efficient breast cancer diagnosis using multi‐level progressive feature aggregation based deep transfer learning system Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-29 Vivek Patel, Vijayshri Chaurasia
Breast cancer is a worldwide fatal disease that exists mostly among women. The deep learning technique has proven its effectiveness, but the performance of the existing deep learning systems is quite compromising. In this work, a deep transfer learning system is suggested for efficient breast cancer classification from histopathology images. This system is based on a novel multi‐level progressive feature
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Examining the classification performance of pre‐trained capsule networks on imbalanced bone marrow cell dataset Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-29 Nesrin Aydin Atasoy, Amina Faris Abdulla Al Rahhawi
The automatic detection of bone marrow (BM) cell diseases plays a vital role in the medical field; it helps to make diagnoses more precise and effective, which leads to early detection and can significantly improve patient outcomes and increase the chances of successful intervention. This study proposed a fully automated intelligent system for BM classification by developing and enhancing Capsule Neural
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Simplifying vein detection for intravenous procedures: A comparative assessment through near‐infrared imaging system Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-29 Atiqa Saeed, Muhammad Rehan Chaudhry, Muhammad Umair Ahmad Khan, Muhammad Ahsan Saeed, Ayman A. Ghfar, Muhammad Naveed Yasir, Hafiz Muhammad Salman Ajmal
The intravenous (IV) injection procedure can be a challenging task, especially for individuals with thin veins, obesity, or patients with damaged and pigmented skin. Therefore, the IV procedure necessitates a portable medical device that can be used for academic demonstrations to train medical students or by health care professionals to perform venipuncture. Vein visualization with a vein detector
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Real‐time small bowel visualization quality assessment in wireless capsule endoscopy images using different lightweight embeddable models Int. J. Imaging Syst. Technol. (IF 3.3) Pub Date : 2024-03-29 Vahid Sadeghi, Alireza Mehridehnavi, Yasaman Sanahmadi, Sajed Rakhshani, Mina Omrani, Mohsen Sharifi
Wireless capsule endoscopy (WCE) captures huge number of images, but only a fraction are medically relevant. We propose automated real‐time small bowel visualization quality (SBVQ) assessment to eliminate transmission of irrelevant frames. Our aim is to design lightweight color‐based models for segmenting clean and contaminated regions with minimal parameters, short training, and fast inference, suitable
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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