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Deep learning shows promise for medical image analysis but lacksinterpretability, hindering adoption in healthcare. Attribution techniques thatexplain model reasoning may increase trust in deep learning among clinicalstakeholders. This paper aimed to evaluate attribution methods for illuminatinghow deep neural networks analyze medical images. Using adaptive path-basedgradient integration, we attributed predictions from brain tumor MRI andCOVID-19 chest X-ray datasets made by recent deep convolutional neural networkmodels. The technique highlighted possible biomarkers, exposed model biases,and offered insights into the links between input and prediction. Our analysisdemonstrates the method's ability to elucidate model reasoning on thesedatasets. The resulting attributions show promise for improving deep learningtransparency for domain experts by revealing the rationale behind predictions.This study advances model interpretability to increase trust in deep learningamong healthcare stakeholders.

Visual Interpretable and Explainable Deep Learning Models for Brain Tumor MRI and COVID-19 Chest X-ray Images