Unraveling the Black Box: A Review of Explainable Deep Learning Healthcare Techniques

Abstract

The integration of deep learning in healthcare has propelled advancements in diagnostics and decision support. However, the inherent opacity of deep neural networks (DNNs) poses challenges to their acceptance and trust in clinical settings. This survey paper delves into the landscape of explainable deep learning techniques within the healthcare domain, offering a thorough examination of deep learning explainability techniques. Recognizing the pressing need for nuanced interpretability, we extend our focus to include the integration of fuzzy logic as a novel and vital category. The survey begins by categorizing and critically analyzing existing intrinsic, visualization, and distillation techniques, shedding light on their strengths and limitations in healthcare applications. Building upon this foundation, we introduce fuzzy logic as a distinct category, emphasizing its capacity to address uncertainties inherent in medical data, thus contributing to the interpretability of DNNs. Fuzzy logic, traditionally applied in decision-making contexts, offers a unique perspective on unraveling the black box of DNNs, providing a structured framework for capturing and explaining complex decision processes. Through a comprehensive exploration of techniques, we showcase the effectiveness of fuzzy logic as an additional layer of interpretability, complementing intrinsic, visualization, and distillation methods. Our survey contributes to a holistic understanding of explainable deep learning in healthcare, facilitating the seamless integration of DNNs into clinical workflows. By combining traditional methods with the novel inclusion of fuzzy logic, we aim to provide a nuanced and comprehensive view of interpretability techniques, advancing the transparency and trustworthiness of deep learning models in the healthcare landscape.

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