Integrated Transfer Learning Algorithms for Breast Cancer Nuclei Segmentation in Histopathological Images

Abstract

Exact and precise segmenting of nuclei in cancer diagnostics is a very important aspect of computer-assisted diagnosis and grading of carcinoma of the breast. Nonetheless, obscure medical findings pose a few challenges in training deep neural networks from the beginning. Through this research, we put forward a combination of deep learning structures on the basis of U-Net for automated breast nuclei segmentation in H&E stained histological findings and pictures, with a little more concentration on Triple Negative Breast Cancer (TNBC). The proposed structures were evaluated Baseline U-Net and U-Net types employing pretrained encoder backbones (VGG16, ResNet50, EfficientNetB0, and MobileNetV2). These different were assessed using Intersection over Union (IoU), Dice coefficient, precision based on picture elements in capturing photos, Precision–Recall evaluation, and ROC-AUC.