COVID-19 and Other Lung Diseases Classification in X-Ray Images Using Machine Learning Algorithms

Abstract

The rapid emergence of communicable diseases continues to pose significant challenges to global health systems, making early detection and timely intervention critical to minimising their spread and impact. This study explores the application of artificial intelligence (AI) and machine learning (ML), focusing on transfer learning with pretrained models, for predicting and detecting communicable diseases from medical imaging data. The research employs models such as EfficientNetB3, adapting them to specific disease datasets to improve prediction accuracy and reduce diagnostic delays. Advanced ML algorithms, including Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Networks (ANN), are utilised to enhance disease detection for respiratory conditions and other communicable diseases. Performance evaluation of the models demonstrates their ability to provide rapid, real-time disease detection, offering valuable tools for healthcare systems in managing outbreaks. This study underscores the transformative potential of AI in healthcare, particularly in disease detection, by enabling faster diagnoses, more effective resource allocation, and improved public health outcomes.