Deep Learning for Breast Cancer Diagnosis Using Histopathological Images

Abstract

Deep learning capabilities with convolution neural networks are unlimited in achieving absolute learning results in all kinds of medical imaging methods. The algorithmic mechanism on datasets is prudent enough to qualify their efficiency. Many random textures and structures are found in the histopathological images of breast cancer that deal with multi-color and multi-structure components. Most of the experiments performed in the wet labs derive results conventionally, but when assisted with the computation models of learning, the accuracy, reliability, and specificity of the results are boosted empirically. The process of employing the computational methods using convolution neural networks in parallel to the conventional experimentation of classification for diagnosing malign breast cancer images attains satisfactory results for effective decision-support.