Chemistry Commons^™

An Explainable Deep Learning Model For Prediction Of Severity Of Alzheimer's Disease, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Chemistry Faculty Research & Creative Works

Deep Convolutional Neural Networks (CNNs) have become the go-To method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. Despite the high predictive accuracy, usability lags in practical applications due to the black-box model perception. Model explainability and interpretability are essential for successfully integrating artificial intelligence into healthcare practice. This work addresses the challenge of an explainable deep learning model for the prediction of the severity of Alzheimer's disease (AD). AD diagnosis and prognosis heavily rely …