A Lightweight Convolutional Neural Network for Breast Cancer Detection Using Knowledge Distillation Techniques

Modu, Falmata (2023-01-15)

Main Thesis


The second most heterogeneous cancer ever discovered is Breast Cancer (BC). BC is a disease that develops from malignant tumors when the breast cells begin to grow abnormally. Although it grows in the breast, it can spread to other body parts or organs. through the lymph and blood vessels of the breast. Globally, more than two million new cases and about 600,000 women died from BC in 2020. Early detection increases the chance of survival by 99%. Deep Learning (DL) models have recorded remarkable achievements in disease diagnosis and treatments. However, it requires powerful computing resources. In this work, we propose a lightweight DL model that can detect BC using the knowledge distillation technique. The knowledge of a pre-trained deep neural network is distilled to a shallow neural work that is easily deployable in a low-power computing environment. We have achieved an accuracy of up to 99%. In addition, we recorded 99% reduction in trainable parameters compared to deploying with a deep neural network