10: Vision Transformer Network Code in MATLAB

The Vision Transformer (ViT) model is an advanced transformer model designed for image classification and serves as the foundation for various computer vision tasks, including object detection. The ViT support package includes three different versions of the model: 1) Base-16, 2) Small-16, and 3) Tiny-16. Each model has been trained on the ImageNet dataset with an input resolution of 384 pixels, and their corresponding pretrained models are stored as .MAT files.

Matlab Code:

clc;

clear all;

close all;

reset(gpuDevice);

gpuDevice(1);

numClass = 100;

net = visionTransformer("tiny-16-imagenet-384"); % Load tiny_ViT model

% net = visionTransformer("small-16-imagenet-384"); % Load small_ViT model

% net = visionTransformer("base-16-imagenet-384"); % Load base_ViT model

lgraph = layerGraph(net); % Extract the layer graph from the trained network and plot the layer graph.

net.Layers(1); % Getting 1st layer information

inputSize = net.Layers(1).InputSize; % getting image size info

lgraph = removeLayers(lgraph, {'head','softmax'});

newLayers = [ 

   fullyConnectedLayer(numClass,'Name','fc','WeightLearnRateFactor',10,'BiasLearnRateFactor',10)

   softmaxLayer('Name','softmax')

   classificationLayer('Name','classoutput')];

lgraph = addLayers(lgraph,newLayers);

lgraph = connectLayers(lgraph,'cls_index' ,'fc');

analyzeNetwork(net)

9: CBMIR

Effective Diagnosis and Treatment through Content-based Medical Image Retrieval by Using Artificial Intelligence

Available at: https://github.com/Owais786786

8: GASTRO-Net

Artificial Intelligence-based Classification of Multiple Gastrointestinal Diseases Using Endoscopy Videos for Clinical Diagnosis

Available at: https://github.com/Owais786786

7: ENDO-Net

Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework with a Large Endoscopic Database: Model Development and Validation

Available at: https://github.com/Owais786786

6: SDCNN

Comprehensive Computer-Aided Decision Support Framework to Diagnose Tuberculosis from Chest X-Ray Images: Data Mining Study

Available at: https://github.com/Owais786786

5: LWE-Net

Light-weighted Ensemble Network with Multilevel Activation Visualization for Robust Diagnosis of COVID19 Pneumonia from Large-scale Chest Radiographic Database

Available at: https://github.com/Owais786786

4: MDA-BN

Multilevel Deep-Aggregated Boosted Network to Recognize COVID-19 Infection from Large-Scale Heterogeneous Radiographic Data

Available at: https://github.com/Owais786786

3: DAL-Net

Domain-Adaptive Lightweight Network for Effective Recognition of Trivial COVID-19 Lesions in Chest Computed Tomography Scans

Available at: https://github.com/Owais786786

2: DMDF-Net

DMDF-Net: Dual Multiscale Dilated Fusion Network for Accurate Segmentation of Lesions Related to COVID-19 in Lung Radiographic Scans 

Available at Github: https://github.com/Owais786786/DMDF-Net

1: MDA-BN-Model

Multilevel Deep-Aggregated Boosted Network to Recognize COVID-19 Infection from Large-Scale Heterogeneous Radiographic Data

Available at Github: https://github.com/Owais786786/MDA-BN-Model