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Comparative Analysis of Different Deep Convolutional Neural Network Architectures for Classification of Brain Tumor on Magnetic Resonance Images

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Abstract

In the current study, the capability of pre-trained Deep Convolutional Neural Network (DCNN) by ImageNet features is proposed for categorization of brain tumors by utilizing MR images. The pre-trained models like ResNet50, InceptionV3, Xception, DenseNet121, MobileNetV3Large, EffcientNetB0, EfficientNetV2L, EfficientNetV2B0 have been exploited for classification purpose. The selection criteria is based upon the diverse proficiency each model depicts. For e.g., EfficientNetB0, ResNet50, MobileNet, Xception employ lower number of training parameters that makes them time efficient whereas, VGG16 though has higher number of training parameters, while increasing the training time without compromising with accuracy. The AlexNet on the other hand also has a reduced number of parameters in comparison to Google’s Inception module which is more memory efficient. AlexNet takes more memory for training. The EfficientNet’s are based on inverted residual blocks of MobileNetV2 with addition to Squeeze and Excitation blocks which makes them highly efficient for feature extraction. Therefore, in this research study, the above stated pre-trained models are used in by retaining the ReLU as an activation function due to its unbounded nature which also helps the model from Vanishing Gradient problem. By hyper tuning the top layers of different pre-trained models by adding Global Average Pooling layer and Dropout layer along with Fully Connected layer with classifier as SoftMax increases the overall efficiency and also reduces overfitting. The proposed comparative study shows the working of different pre-trained DCNN models for classifying brain tumors. The proposed experimental study is performed on three different databases i.e., Kaggle, BraTS 2018, and Real time dataset acquired from PGIMER and comparison analysis. The comparison analysis with existing methods as well as statistical analysis is also performed. It is observed from the results that EfficientNetB0 has outperformed all the existing methods. The pre-trained EfficientNetB0 architecture by hyper tuning the parameters, the testing accuracy has increased by 2.14% on Kaggle and increased the classification accuracy by 3.98% on BraTS dataset. On Real time dataset comprising of Glioblastoma Multiforme and Oligodendroglioma, highest accuracy of 97.32% is achieved among all other models.

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Abbreviations

AI:

Artificial intelligence

CAD:

Computer aided diagnosis

DCNN:

Deep convolutional neural network

CNN:

Convolutional neural network

WCNN:

Wavelet convolutional neural network

MRI:

Magnetic resonance imaging

CT:

Computed tomography

NMR:

Nuclear magnetic resonance

RF:

Radio frequency

ML:

Machine learning

ELM:

Extreme learning machine

PSO:

Particle swarm optimization

KSVM:

Kernel support vector machine

PCA:

Principal component analysis

DWT:

Discrete wavelet transformation

CV:

Cross validation

PPCA:

Probabilistic principal component analysis

SHO:

Spotted hyena optimization

ANN:

Artificial neural network

SVM:

Support vector machine

GA:

Genetic algorithm

SCA:

Sine cosine algorithm

HGG:

High grade Glioma

LGG:

Low grade Glioma

GBM:

Glioblastoma multiforme

OGM:

Oligodendroglioma

CE:

Categorical crossentropy

GAP:

Global average pooling

TPR:

True positive rate

TNR:

True negative rate

PPV:

Positive predicted value

NPV:

Negative predicted value

FPR:

False positive rate

FNR:

False negative rate

ACC:

Accuracy

MCC:

Mathew’s correlation coefficient

k:

Cohen’s Kappa coefficient

MANet:

Multilevel attenuation network

SENet:

Squeeze and excitation network

SGDM:

Stochastic gradient descent with momentum

NifTI:

Neuroimaging informatics technology initiative

PGIMER:

Post-graduate Institute of Medical Education and Research

ADBRF:

Adaboost random forest

BSO:

Brain-storm optimization

VG:

Vanishing gradient

NADE:

Neural autoregressive distribution estimation

PET:

Positron emission tomography

ILSVRC:

ImageNet large scale visual recognition channel

RBF:

Radial basis function

Dolphin SCA:

Dolphin echolocation based sine cosine algorithm

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Funding

The authors gratefully acknowledge financial support from Indian Council of Medical Research: (ICMR)/ISRM (12)46/2019.

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Authors and Affiliations

  1. Department of Electrical and Instrumentation Engineering, TIET, Patiala, India

    Jainy Sachdeva

  2. Department of Radiodiagnosis & Imaging, PGIMER, Chandigarh, India

    Deepanshu Sharma & Chirag Kamal Ahuja

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  1. Jainy Sachdeva
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Correspondence to Chirag Kamal Ahuja.

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Sachdeva, J., Sharma, D. & Ahuja, C.K. Comparative Analysis of Different Deep Convolutional Neural Network Architectures for Classification of Brain Tumor on Magnetic Resonance Images. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-023-10041-y

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