Interpretable Medical Image Diagnosis Methodology using Convolutional Neural Networks and Bayesian Networks
Vasilios Zarikas, Spiros V. Georgakopoulos · 2024
A novel general methodology for fully interpretable AI concerning medical images is analyzed. The various consecutive steps of the method are presented in an instructive way. The algorithm is quite general, covering most of the cases of medical images. Interpretable diagnosis based on medical images is particularly useful for demanding applications like medical ones. Results show an efficient performance.