Postgraduate Studies

In recent years, deep learning models have provided various applications in various fields, especially in the medical fields such as neuroscience. Thus, ensuring the interpretation of the results predicted by deep learning models has been an important challenge, especially exploring the hidden layers in these models, and this is called black box interpretation.

This thesis presents an integrated two-step learning framework that uses deep learning to simplify and increase the accuracy of the breast cancer diagnosis process. This innovative methodology differs from traditional methods by using raw, unmarked X-ray images, and this would facilitate the pre-treatment phase.