Machine Learning-based Classification and Analysis of Breast Cancer Pathological Images
Keywords:
breast cancer, histopathology, nuclear segmentation, machine learning, CNNAbstract
Histopathology is the microscopic examination of diseased cells and tissues present in the body. Determining the degree of severity of illness is very essential in the diagnosis process. Assessing the variations that take place during the illness requires an in-depth understanding of the fundamental structures and functions of various tissues. This study aimed to develop a diagnostic tool to aid clinicians in the preliminary digital examination of histopathological breast tissue slides obtained after surgical biopsy. The designed study was carried out on the open-source Breakhis dataset. The breast tumor microscopic tissue images were divided into 5 categories, including benign breast tumors and 4 different types of malignant breast tumors (lobular carcinoma, ductal carcinoma, papillary carcinoma, and mucinous carcinoma). The images were prepared for classification by performing preprocessing steps such as color normalization and nuclei segmentation using the K-means algorithm. A comparative analysis was performed using four image classification algorithms: AlexNet, Convolutional Neural Networks (CNN), Support Vector Machine (SVM), and K-nearest Neighbors (KNN). The AlexNet performed at a significantly higher caliber with an accuracy of 98%. A GUI, or graphic user interface, was created as an assist platform for physicians to utilize the designed breast tumor tissue image analysis pipeline.
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