Deep learning methods for automatic classification of medical images and disease detection based on chest X-Ray images
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Detecting and classifying diseases using X-Ray images is one of the more challenging core tasks in the medical and research world. Innovations and revolutions of Computer Vision with Deep learning methods offer great promise for fast and accurate diagnosis of screening and detection from chest X-Ray images (CXR). This work presents rapid detection of diseases in the lung using the efficient Deep learning pre-trained RepVGG algorithm for deep feature extraction and classification. We performed automatic classification of X-Ray images into three categories as Covid-19, Pneumonia, and Normal X-Ray cases. For evaluation, first, we used a histogram-oriented gradient (HOG) to detect the shape of the region of interest (ROI). We used the ROI object to improve the detection accuracy for lung extraction, followed by data pre-processing and augmentation. Then a pre-trained RepVGG model is used for deep feature extraction and classification, similar to VGG and ResNet convolutional neural network for the training-time and inference-time architecture transformed from the multi to the flat mode by a structural re-parameterization technique. Next, using the Computer Vision technique, we created a feature map and superimposed it on the original images. We used this technique for the automatic highlighted detection of affected areas of people's lungs. Based on the X-Ray images, we developed an algorithm that classifies X-Ray images with height accuracy and power faster thanks to the architecture transformation of the model. We compare deep learning frameworks' accuracy and detection of disease. The study shows the high power of deep learning methods for X-Ray images based on COVID-19 detection utilizing chest X-Ray. The proposed framework shows better diagnostic accuracy by comparing popular deep learning models, i.e., VGG, ResNet50, inceptionV3, DenseNet, and InceptionResnetV2.
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