BREAST CANCER DETECTION AND CLASSIFICATION USING HISTOPATHOLOGICAL IMAGES BASED ON OPTIMIZATION-ENABLED DEEP LEARNING

Cancer is the second cause of mortality worldwide and it has been identified as a perilous disease. Among all types of cancers, Breast Cancer (BC) is a substantial research subject in the medical imaging area, because it is a serious disease and primary reason for death in women. Proper diagnosis helps patients to get adequate treatment, enhancing the probability of surviving. Because of the poor contrast and unclear structure of tumor cells in the images, automatic segmenting of breast tumors remains a difficult task. However, the identification and interpretation of breast lesions is challenging even for expert radiologists. To address these limitations, an efficient mechanism for BC detection and classification using histopathological images is proposed, which employs a DenseNet-based Chronological Circle Inspired Optimization Algorithm (CCIOA). Deep Learning (DL) approaches are used in the suggested BC classification scheme to precisely segment and identify the BC. The segmentation is done using ResuNet++, and an efficient optimization method called Invasive Water Ebola Optimization (IWEO) is used to fine-tune the DL network's parameters. Furthermore, DenseNet is utilized for BC detection, while CCIOA is used for DenseNet training. The CCIOA-DenseNet is evaluated using the metrics of accuracy, True Positive Rate (TPR), and True Negative Rate (TNR). Experiment results show that the CCIOA-DenseNet attained better accuracy of 0.971, TPR of 0.966, and TNR of 0.954.

Automated summary

Breast cancer is a serious disease and a significant research subject in the medical imaging field. Automatic segmentation of breast tumors is challenging due to poor contrast and unclear structure of tumor cells in images. To address this, a mechanism using histopathological images for breast cancer detection and classification is proposed.