Design and Development of an Integrated Healthcare Platform Using Deep Convolutional Neural Networks (DCNNs)

The global burden of chest diseases, particularly tuberculosis (TB), continues to be a significant concern, contributing to a substantial number of fatalities worldwide. Early detection of these ailments is critical for effective treatment and management. In response to this imperative, this book chapter proposes a novel approach leveraging deep convolutional neural network (DCNN) technology for the extraction of discriminative features and classification of chest X-ray images to diagnose pneumonia and tuberculosis. The proposed model is trained on a diverse dataset comprising images depicting normal, pneumonia and tuberculosis cases, achieving superior accuracy rates compared to existing methodologies. Furthermore, it outlines the architectural framework of the proposed DCNN model and conducts a comparative analysis with established pretrained models, including AlexNet, GoogLeNet and ResNet101, as well as a bespoke bag of feature models. This comparative evaluation is conducted utilizing standardized image datasets, ensuring methodological rigour. The implementation of the proposed DCNN model offers tangible benefits to healthcare professionals, facilitating prompt diagnosis and intervention. Through evaluation and comparison with established benchmarks, we underscore the superior performance and efficiency of our approach in terms of diagnostic accuracy, training and validation metrics, convergence characteristics and error rates.