Computer-assisted frameworks for classification of liver, breast and blood neoplasias via neural networks: A survey based on medical images.

Computer Aided Diagnosis (CAD) systems can support physicians in classifying different kinds of breast cancer, liver cancer and blood tumours also revealed by images acquired via Computer Tomography, Magnetic Resonance, and Blood Smear systems. In this regard, this survey focuses on papers dealing with the description of existing CAD frameworks for the classification of the three mentioned diseases, by detailing existing CAD workflows based on the same steps for supporting the diagnosis of these tumours. In detail, after an appropriate acquisition of the images, the fundamental steps carried out by a CAD framework can be identified as image segmentation, feature extraction and classification. In particular, in this work, specific CAD frameworks are considered, where the task of feature extraction is performed by using both traditional handcrafted strategies and Convolutional Neural Networks-based innovative methodologies, whereas the final supervised pattern classification is based on neural/non-neural machine learning methods. The cited methodology is focused on sharing and reviewing an amount of specific works. Then, the performance of three selected case studies are carefully reported, designed with the aim of showing how final outcomes can vary according to different choices in each step of the adopted workflow. More in detail, these case studies concern with breast images acquired by Tomosynthesis and Magnetic Resonance, hepatocellular carcinoma images acquired by Computed Tomography and enhanced by a triphasic protocol with a contrast medium, peripheral blood smear images for cellular blood tumours and are used to compare their performance.