An Explainable CNN Framework for Efficient Pneumonia Detection from Chest X-Rays

Abstract

Pneumonia is a respiratory condition, which is critical and needs an accurate diagnosis to minimize death. The following paper introduces a deep learning model, which can be explained, to identify pneumonia in the X-ray images of the chest, using the Convolutional Neural Networks (CNN) architecture. The image resizing, normalization and data augmentation are some of the preprocessing strategies included in the proposed approach to improve the quality of data and the generalization of the model. CNN model is created to automatically identify discriminative features and binomial classification between normal and pneumonia cases with the help of a binary cross-entropy loss and a sigmoid activation function. Gradient-weighted Class Activation Mapping (Grad-CAM) is added to enhance the interpretability of the model by producing visual explanations by highlighting the areas that do affect the model predictions. The results of the experiment prove that the proposed model can show high classification rates (up to 97 percent) and can compete with those of the pretrained models like VGG16, ResNet50, and DenseNet121, at the same time, having lower computational complexity. The suggested framework offers high precision, as well as interpretability, and is an effective instrument to use in the process of computer-aided diagnosis and assist in clinical decision-making in the field of medical imaging.