Multi Disease Diagnosis Model for Chest X-ray Images with Explainable AI – Grad-Cam Feature Map Visualization

In this work, we have proposed a deep convolutional neural network model with the background of a network as ResNet and a classification block as a faster RCNN model to predict multiple lung cancer abnormalities from X-ray images. Experimentations are conducted on Kaggle image dataset repositories with X-ray images of about 55000 images comprising of 15 different classes. We have proposed a region proposal network capable of autonomously learning from the pre-trained data specifications and applying a bounding box to cancer-affected regions to detect the affected areas. About 80% of samples were considered to conduct training from each class, and 20% were used for testing. The results obtained have an accuracy of about 96% for classification.