Toward explainable deep learning in healthcare through transition matrix and user-friendly features

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dc.contributor.authorBarmak, Oleksander
dc.contributor.authorKrak, Iurii
dc.contributor.authorYakovlev, Sergiy
dc.contributor.authorManziuk, Eduard
dc.contributor.authorRadiuk, Pavlo
dc.contributor.authorKuznetsov, Vladislav
dc.date.accessioned2024-11-26T12:00:28Z
dc.date.available2024-11-26T12:00:28Z
dc.date.issued2024-11-25
dc.description.abstractModern artificial intelligence (AI) solutions often face challenges due to the “black box” nature of deep learning (DL) models, which limits their transparency and trustworthiness in critical medical applications. In this study, we propose and evaluate a scalable approach based on a transition matrix to enhance the interpretability of DL models in medical signal and image processing by translating complex model decisions into user-friendly and justifiable features for healthcare professionals. The criteria for choosing interpretable features were clearly defined, incorporating clinical guidelines and expert rules to align model outputs with established medical standards. The proposed approach was tested on two medical datasets: electrocardiography (ECG) for arrhythmia detection and magnetic resonance imaging (MRI) for heart disease classification. The performance of the DL models was compared with expert annotations using Cohen’s Kappa coefficient to assess agreement, achieving coefficients of 0.89 for the ECG dataset and 0.80 for the MRI dataset. These results demonstrate strong agreement, underscoring the reliability of the approach in providing accurate, understandable, and justifiable explanations of DL model decisions. The scalability of the approach suggests its potential applicability across various medical domains, enhancing the generalizability and utility of DL models in healthcare while addressing practical challenges and ethical considerations.
dc.identifier.citationToward explainable deep learning in healthcare through transition matrix and user-friendly features / O. Barmak et a. Frontiers in Artificial Intelligence. 2024. Vol. 7. P. 1482141. URL: https://doi.org/10.3389/frai.2024.1482141
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/17107
dc.language.isoen
dc.publisherFrontiers Media SA
dc.subjecthealthcare
dc.subjectartificial intelligence
dc.subjectdeep learning
dc.subjectmedical signal processing
dc.subjectmedical image analysis
dc.subjectmodel interpretability
dc.titleToward explainable deep learning in healthcare through transition matrix and user-friendly features
dc.title.alternativeToward explainable deep learning in healthcare through transition matrix and user-friendly features
dc.typeСтаття
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