Table 2

- Summary of CADS developed for MS using MRI neuroimaging modalities and details of deep learning architectures.

Article	Preprocessing toolbox	Others preprocessing	Toolbox	K Fold	Details	Classifier	Loss function	Optimizer
Afzal el al., 2018²³	-	data augmentation	Keras	-	6 convolutional layers + 6 Max Pooling	-	-	Proposed
Afzal el al., 2021²⁴	FMRIB	Patch Extraction	Keras, Tensor Flow	-	2 convolutional layers + 2 Max Pooling + 1 fully connected	Multinomial LR	-	-
Alijamaat et al., 2020²⁵	-	data augmentation, Histogram Stretching, discrete wavelet transform	Keras, Tensor Flow	-	15 convolutional layers + 1 Average Pooling + 1 fully connected + Dropout	Sigmoid	-	Adam
Aslani et al., 2019²⁶	FMRIB	Decomposing 3D Data Into 2D Images	Keras, Tensor Flow	4	3 Parallel ResNet50s + 5 MMFF Blocks + 4 MSFU Blocks + MPR Block	Softmax	Soft Dice Loss function	Adam
Aslani et al., 2019²⁷	-	Data Augmentation	Keras	-	ResNet50 + UFF Blocks	-	binary cross-entropy	Adadelta
Coronado et al., 2020²⁸	-	Magnetic Resonance Imaging Automatic Processing Pipeline	-	-	5 convolutional + 4 Context Modules + 3 Up Sampling Modules + 2 Localization Modules + 2 Segmentation + 3 Strides + 3 De-Conv + 1 Upscaling	Softmax	Multiclass Weighted Dice	Adam
Eitel et al., 2019²⁹	FMRIB	data augmentation	Keras, Tensor Flow	-	4 convolutional + 4 Max-Pooling + 4 Dropout	Sigmoid	-	Adam
Kazancli et al., 2018³⁰	Free Surfer	Patch Extraction	Tensor Flow	-	2 convolutional + 2 Average Pooling + 2 batch normalization + 1 fully connected + 1 Dropout	Softmax	cross-entropy	Adam
La Rosa et al., 2018³¹	FMRIB	Manual Segmentation, LeMan-PV	-	-	4 convolutional + 2 Max Pooling + 4 batch normalization + 1 fully connected + 1 Dropout	Softmax	cross-entropy	Adam
Roy et al., 2018³²	-	-	Tensor Flow, Keras	-	15 convolutional	-	-	Adam
Shrwan et al., 2021³³	-	-	Matlab R2020a	-	3 convolutional + 3 batch normalization + 3 Max Pooling + 2 fully connected	Softmax	cross-entropy	SGDM
Siar et al., 2019³⁴	-	-	-	-	25 Layers	Softmax	-	-
Valverde et al., 2018³⁵	FMRIB	-	Keras, Tensor Flow	-	4 convolutional + 2 Max-Pooling + 4 batch normalization + 3 fully connected + 3 Dropout	Softmax	categorical cross-entropy	ADADELTA
Wang et al., 2018³⁶	-	histogram stretching, data augmentation	-	-	11 convolutional + 11 batch normalization + 4 Pooling + 3 fully connected + 2 Dropout	Softmax	-	-
Zhang et al., 2018³⁷	-	histogram stretching, data augmentation	-	-	7 convolutional +7 Pooling + 3 fully connected + 3 Dropout	Softmax	-	-

ADADELTA: adaptive learning rate method, Adam: A Method for Stochastic Optimization, CADS: Computer-aided detection software, CDMS: clinically defined multiple sclerosis, EDSS: Expanded Disability Status Scale, FMRIB: Functional Magnetic Resonance Imaging of the Brain, MMFF: multi-modal feature fusion block,, MRI: Magnetic resonance imaging, MRIAP: Magnetic Resonance Imaging Automatic Processing, MSFU: multi-scale feature upsampling block, MPR: multi-planes reconstruction, Matlab: matrix laboratory, SGDM: Stochastic Gradient Descent Momentum, UFF: upsampling fused featu