Smartrad-Xai

Abstract

The early detection of chest diseases from X-ray images vital for timely clinical decision making especially in resource-limited health care systems where the availability of expert radiologists can be limited. The current computer-aided diagnostic systems have some drawbacks, including limited real-time access, limited clinical validation and reduced generalization between diverse disease categories. This study presents an Automated Chest Disease Classification framework, named SmartRadXAI, which combines deep learning and the mobile health care technology to address the challenges and provide reliable and easy-to-use diagnosis. In this research work, we introduce the automated chest disease classification system based on pretrained deep learning models: ResNet18, ResNet50, Vision Transformer (ViT), YOLOv8s, and YOLOv8n, which is called SmartRad-XAI. The fiveclass disease classification was done on ChestX-ray14 dataset from the NIH. By enhance accuray and precision of model, the data was preprocessed using Roboflow, which involves resizing images and augmenting data sets. Standard classification metrics were used to train all models and compare their results. Experiments demonstrated that YOLOv8s had the best performance, and the light version YOLOv8n was not as accurate as the other models. Besides the deep learning framework, a mobile app was designed to offer real-time chest X-ray analysis. The app enables users to upload photos by using the camera or gallery, then get predictions of which diseases are detected, with levels of confidence, and manage their prediction history. In addition, the patients X-Ray results were verified with the help of Islamabad Diagnostic Centre (IDC) where its predictions were compared with the interpretation of the patient by expert radiologist for the purpose of assessing its applicability in practical applications. As a whole, the study SmartRad-XAI highlights the potential of merging deep learning with mobile healthcare technology to deliver reliable, efficient, and accessible AI-powered chest disease diagnosis both in experimental and real clinical settings