An Introductory Implementation of Breast Cancer Detection from Mammograms and Pixel Intensity with Efficient-Net Other Neural Nets

Abstract

In the world of civilized medical scientific progression, cancer has become a very serious threat for the natural survival of human beings where breast cancer stays to be the second most dangerous type. Mostly women are embracing very pathetic death because of the delayed detection of the cancer cell in the certain period of their life. Machine Learning mechanism can definitely help at the stage of medical imaging which can escalate the diagnosis of the cancer cells at a very early age of its biological formation and development. We focused upon the deep learning approach to classify the normal and abnormal breast according to the medical imaging from the MIAS dataset of Mammograms and Pixel Intensity. The Convolution Neural Network (CNN) alongside ResNet, AmoebaNet and EfficientNet have been used for the detection with 330 mammograms in which 194 images are normal and 136 are having the identification of abnormal breasts. The accuracy of the entire experimental results was carrying the torch of potential legacy of deep learning in the medical imaging arena. The research is ongoing for the further development and optimization of CNN, AmoebaNet-C and EfficientNet architecture for the Pixel Intensity with higher accuracy, proper segmentation and masking. Source code of this research is available here: https://github.com/ac005sheekar/Breast-Cancer-Detection-with-Pixel-Intensity/