| Deep Learning models have been employed over the past decade to improve the detection of conditions relative to the human body and in relation to breast cancer particularly. However, their application to the clinical domain has been limited even though they improved the detection of breast cancer in women at an early stage. Our contribution attempts to interpret the early detection of breast cancer while enhancing clinicians’ confidence in such techniques through the use of eXplainable AI.We researched the best way to back-propagate a selected CNN model, previously developed in 2017; and adapted in 2019. Our methodology proved that it is possible to uncover the intricacies involved within a model; at neuron level, in converging towards the classification of a mammogram. After conducting a number of tests using five back-propagation methods, we noted that the Deep Taylor Decomposition and the LRP-Epsilon techniques produced the best results. These were obtained on a subset of 20 mammograms chosen at random from the CBIS-DDSM dataset. The results showed that XAI can indeed be used as a proof of concept to begin discussions on the implementation of assistive AI systems within the clinical community. |
*** Title, author list and abstract as seen in the Camera-Ready version of the paper that was provided to Conference Committee. Small changes that may have occurred during processing by Springer may not appear in this window.