Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical Imaging
Abstract
The integration of artificial intelligence (AI) applications in the healthcare sector is ushering in a significant transformation, particularly in developing more effective strategies for early diagnosis and treatment of contagious diseases like tuberculosis. Tuberculosis, a global public health challenge, demands swift interventions to prevent its spread. While deep learning and image processing techniques show potential in extracting meaningful insights from complex radiological images, their accuracy is often scrutinized due to a lack of explainability.
This research navigates the intersection of AI and tuberculosis diagnosis by focusing on explainable artificial intelligence (XAI). A meticulously designed deep learning model for tuberculosis detection is introduced alongside an exploration of XAI to unravel complex decisions.
The core belief is that XAI, by elucidating diagnostic decision rationale, enhances the reliability of AI in clinical settings. Emphasizing the pivotal role of XAI in tuberculosis diagnosis, this study aims to impact future research and practical implementations, fostering the adoption of AI-driven disease diagnosis methodologies for global health improvement.
Keywords
Artificial intelligence, Deep learning, Explainable AI, Medical imaging, Tuberculosis diagnosis
Supporting Institution
Sakarya University of Applied Sciences AI And Data Science Research And Application Center
Ethical Statement
This research adheres to ethical principles and guidelines in conducting the comparative analysis of Explainable Artificial Intelligence (XAI) techniques, specifically SHAP (SHapley Additive exPlanations) and LIME , on tuberculosis x-ray dataset.
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