A Study and Analysis of Deep Neural Networks for Cancer Using Histopathology Images

Abstract

Due to the complexity of histopathology tissues, an accurate classification and segmentation of cancer diagnosis is a challenging task in computer vision. The nuclei segmentation of microscopic images is a key prerequisite for cancerous pathological image analysis. However, an accurate nuclei segmentation is a long running major challenge due to the enormous color variability of staining, nuclei shapes, sizes, and clustering of overlapping cells. To address these challenges and early diagnosis as well as reduce the bias decisions of expert lab technician of cancer in clinical practice, the authors study the classification of computer-aided frameworks and automatic nuclei segmentation frameworks based on histopathology images by convolutional deep learning. The authors have used a publicly available PatchCamelyon and 2018 Data Science Bowl histology image dataset for this study. The results are compared and expected to be useful clinically for technician experts in the analysis of cancer diagnosis and the survival chances of patients.