Machine learning and deep learning for cancer diagnosis
Authors :- Soewu T.; Kaur H.; Sandhu R.; Soni A.; Ghai D.; Dhir K.; Tripathi S.L.
Publication :- Rehabilitation Robotics and Healthcare Devices, Elsevier, 2025, Pages 83-129
Deep neural networks (DNNs) have witnessed a tremendous amount of attention over the last few years in the field of medical image analysis. It includes various medical image modalities like magnetic resonance imaging (MRI), computed tomography (CT) scan, and X-ray and has been widely used in a variety of fields including the identification of tumors and cancer. In this chapter, the contribution of several researchers toward the use of DNNs techniques has been discussed. Along with the recommendations, several difficulties faced during tumor and cancer detection have also been covered. The various steps involved in detecting cancer disease are image classification, image segmentation, transfer learning, and feature fusion. DNNs have also been used to classify and segment a variety of tumors and cancers, but fewer studies have been done on the application of transfer learning and feature fusion for tumor and cancer detection. Researchers should carry out additional studies on transfer learning and feature fusion for tumor and cancer diagnosis to build better-performing models.