Driving Training-Based Optimization- Multitask Fuzzy C-Means (DTBO-MFCM) Image Segmentation and Robust Deep Learning Algorithm for Multicenter Breast Histopathological Images

The second most frequent disease in terms of diagnoses is breast cancer, which has had tremendous impact on women’s lives all around the world. The most frequent cause is a tumour formed as a result of abnormal cell divisions of tissues in the breast where rate of growth and proliferation rate assessed using mitotic activity indices. Image segmentation is regarded as a crucial stage in the processing of images. One of the often-used techniques for picture segmentation is FCM (Fuzzy C-Means) clustering. However, there are problems with this approach, including sensitivity to beginning values, becoming stuck in local optimas, and being unable to tell among objects with identical colour intensity. In order to provide improved generalisation over hitherto unexplored domains, the FMD (Fourier Mitosis Detection) technique has also been added to the shift issue in this study. The three components of the FMD method are fuzzy segmentation-based mitotic detection, pixel-level annotation creation, and Fourier-based data augmentation. For segmentation-based mitosis identification, DTBO-MFCM (Driving Training-Based Optimisation- Multitask Fuzzy C-Means) clustering has been introduced. The DTBO algorithm offers a greater capacity for exploration while looking for the optimum answer to a problem, which avoids the algorithm from becoming stuck in local optimas. This technique is carried out at the image level, therefore there is no need for training and it can be put into use very quickly. The thorough mitosis identification procedure is then carried out on these pictures after they have been cropped into tiny patches. The comparison experiments with other mitosis detections also prove proposed system’s efficacy.