Mitigating Risk in the Application of Machine Learning to the Diagnosis of Bronchopulmonary Diseases

This paper explores the critical issue of risk mitigation in the application of machine learning-based software solutions to image classification, using chest X-rays for diagnosing bronchopulmonary diseases as a case study. The research outlines the challenge of reducing the risk of diagnostic errors by implementing defensive measures against adversarial attacks. Drawing on experimental data from chest X-ray images, this study identifies the most effective machine learning methods for classification, as well as the most threatening attacks that undermine recognition accuracy. Furthermore, it proposes countermeasures designed to mitigate these risks. The experimental findings lead to a set of recommendations, formulated as guidelines that integrate recognition methods, attack types, and countermeasures, aimed at minimizing the risk of misdiagnosis.