Comparative Study of CNN-Based Multi-Disease Detection Models Through X-Ray Images

Abstract

Adaptation of computer-aided techniques in health care is continually improving diagnosis and treatment using chest X-ray images. Deep learning approaches are proving to be effective in offering more accurate disease detection However, there are still significant hurdles in medical imaging. In this paper, presented an experiential comparative analysis of popular deep learning-based convolutional neural networks (CNN’s) models such as ResNet50, Xception, VGG16, and VGG19 using transfer learning for multi-disease detection. Although experimented with several deep convolution architectures but presented here top most only. This paper addresses four classes (chest disease) classification using chest X-ray, namely COVID, Normal, Pneumonia, and Tuberculosis. All four models are trained, tested, and validated using the same chest X-ray dataset which consists of 700 images for each disease. The comparative result presented, accuracy, predict output, training and validation loss, confusion matrices, error rate, and F1-score.