Explainable Artificial Intelligence: Transitioning DL Model Decisions to User-Understandable Features in Healthcare

Скорочена інформація про документ
dc.contributor.authorRadiuk, Pavlo
dc.contributor.authorBarmak, Oleksander
dc.contributor.authorManziuk, Eduard
dc.contributor.authorKrak, Iurii
dc.date.accessioned2024-10-30T18:29:24Z
dc.date.available2024-10-30T18:29:24Z
dc.date.issued2024-10-05
dc.description.abstractModern artificial intelligence (AI) solutions often face problems of 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 for enhancing the interpretability of DL models in medical signal and image processing by translating complex model decisions into features that are understandable to healthcare professionals. The proposed approach was tested on two medical datasets: ECG signals for arrhythmia detection and MRI scans for heart disease classification. The performance of the DL models was compared with expert annotations, using Cohen’s Kappa coefficient as the primary metric to assess agreement. The study found strong agreement between DL model predictions and expert annotations, with Cohen’s Kappa coefficients of 0.89 for the ECG dataset and 0.80 for the MRI dataset, demonstrating the usefulness of the approach in providing reliable and interpretable results. In sum, the proposed scalable approach significantly improves the interpretability of DL models in medical applications.
dc.identifier.citationExplainable artificial intelligence: Transitioning DL model decisions to user-understandable features in healthcare / P. Radiuk et al. 4th International Workshop of IT-professionals on Artificial Intelligence 2024 (ProfIT AI 2024) : CEUR-Workshop Proceedings, Cambridge, MA, USA, 25–27 September 2024 / ed. by D. Chumachenko et al. Vol. 3777. CEUR-WS.org, Aachen, 2024. P. 185–199. URL: https://ceur-ws.org/Vol-3777/paper12.pdf
dc.identifier.issn1613–0073
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/16949
dc.language.isoen
dc.publisherCEUR-WS.org
dc.subjectexplainable artificial intelligence
dc.subjectdeep learning
dc.subjectmedical signal processing
dc.subjectmedical image analysis
dc.subjectmodel interpretability
dc.subjectCohen’s Kappa
dc.titleExplainable Artificial Intelligence: Transitioning DL Model Decisions to User-Understandable Features in Healthcare
dc.typeСтаття
Файли
Контейнер файлів
Зараз показуємо 1 - 1 з 1
Ескіз
Назва:
Radiuk_Explainable-Artificial-Intelligence.pdf
Розмір:
2.15 MB
Формат:
Adobe Portable Document Format
Завантажити
Ліцензійна угода
Зараз показуємо 1 - 1 з 1
Назва:
license.txt
Розмір:
4.26 KB
Формат:
Item-specific license agreed upon to submission
Опис:
Завантажити
Зібрання
Кафедра комп’ютерних наук