Artificial Hummingbird Algorithm with Transfer-Learning-Based Mitotic Nuclei Classification on Histopathologic Breast Cancer Images

• Published in: Bioengineering (Basel) Vol. 10; no. 1; p. 87
• Main Authors: Malibari, Areej A., Obayya, Marwa, Gaddah, Abdulbaset, Mehanna, Amal S., Hamza, Manar Ahmed, Ibrahim Alsaid, Mohamed, Yaseen, Ishfaq, Abdelmageed, Amgad Atta
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 09.01.2023; MDPI
• Subjects: Algorithms; artificial hummingbird algorithm; Artificial intelligence; Bioengineering; Breast cancer; Cell cycle; Classification; Decomposition; Diagnosis; Feature extraction; Histology; Histopathology; histopathology images; Image processing; Image segmentation; Machine learning; Mammography; Medical imaging; Methods; mitotic nuclei classification; Neural networks; Nuclei; Transfer learning; breast cancer; artificial hummingbird algorithm; mitotic nuclei classification; medical imaging; histopathology images
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering10010087

Recently, artificial intelligence (AI) is an extremely revolutionized domain of medical image processing. Specifically, image segmentation is a task that generally aids in such an improvement. This boost performs great developments in the conversion of AI approaches in the research lab to real medical applications, particularly for computer-aided diagnosis (CAD) and image-guided operation. Mitotic nuclei estimates in breast cancer instances have a prognostic impact on diagnosis of cancer aggressiveness and grading methods. The automated analysis of mitotic nuclei is difficult due to its high similarity with nonmitotic nuclei and heteromorphic form. This study designs an artificial hummingbird algorithm with transfer-learning-based mitotic nuclei classification (AHBATL-MNC) on histopathologic breast cancer images. The goal of the AHBATL-MNC technique lies in the identification of mitotic and nonmitotic nuclei on histopathology images (HIs). For HI segmentation process, the PSPNet model is utilized to identify the candidate mitotic patches. Next, the residual network (ResNet) model is employed as feature extractor, and extreme gradient boosting (XGBoost) model is applied as a classifier. To enhance the classification performance, the parameter tuning of the XGBoost model takes place by making use of the AHBA approach. The simulation values of the AHBATL-MNC system are tested on medical imaging datasets and the outcomes are investigated in distinct measures. The simulation values demonstrate the enhanced outcomes of the AHBATL-MNC method compared to other current approaches.