Histopathological breast-cancer image classification based on machine-learning techniques

Machine-Learning (ML) techniques bring a new paradigm which has generated a revolutionary momentum and wrought changes in every day of our modern lives, ranging from autonomous lifestyles to decision-making scenarios. Among the different branches of ML activities, the medical field is notable, covering the field of detection and monitoring as well as the present status of diseases. Among the different medical diseases cancer is a serious threat. In particular, breast cancer is always a serious threat to women. Proper identification and then proper management and monitoring help the patient to recover from the disease, or at least help them to lead a better life. Proper identification and the current status of cancer largely depend on biomedical image analysis, a complex area of understanding. The analysis of these images requires special knowledge. The autonomous finding of Benign and Malignant information based on the images and making a Computer-Aided Diagnosis (CAD) system provide both the patient and the doctor with a second layer of confidence and allow them to make a more reliable decision. For the autonomous identification and detection of cancer, digital ML techniques have provided a revolutionary improvement. The recent development of the Deep Neural Network (DNN) and the logic-based algorithm make it possible to detect the target form the image more reliably. In this thesis we have investigated the performance of the DNN-based biomedical image classiffer as well as the Extreme Gradient Boosting (XGBoost)-based image classiffierfor the autonomous CAD system.