Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning

• Main Authors: Yiğit, Tuncay, Şengöz, Nilgün, Özmen, Özlem, Hemanth, Jude, Işık, Ali Hakan
• Format: Journal Article
• Language: English
• Published: 02-08-2022
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning
• DOI: 10.48550/arxiv.2208.01674

Traitement du Signal, Vol. 39, No. 3, pp. 863-869, 2022 Artificial intelligence holds great promise in medical imaging, especially
histopathological imaging. However, artificial intelligence algorithms cannot
fully explain the thought processes during decision-making. This situation has
brought the problem of explainability, i.e., the black box problem, of
artificial intelligence applications to the agenda: an algorithm simply
responds without stating the reasons for the given images. To overcome the
problem and improve the explainability, explainable artificial intelligence
(XAI) has come to the fore, and piqued the interest of many researchers.
Against this backdrop, this study examines a new and original dataset using the
deep learning algorithm, and visualizes the output with gradient-weighted class
activation mapping (Grad-CAM), one of the XAI applications. Afterwards, a
detailed questionnaire survey was conducted with the pathologists on these
images. Both the decision-making processes and the explanations were verified,
and the accuracy of the output was tested. The research results greatly help
pathologists in the diagnosis of paratuberculosis.