Applications of Machine Learning in Cancer Imaging: A Review of Diagnostic Methods for Six Major Cancer Types

Abstract

Machine learning (ML) methods have revolutionized cancer analysis by enhancing the accuracy of diagnosis, prognosis, and treatment strategies. This paper presents an extensive study on the applications of machine learning in cancer analysis, with a focus on three primary areas: a comparative analysis of medical imaging techniques (including X-rays, mammography, ultrasound, CT, MRI, and PET), various AI and ML techniques (such as deep learning, transfer learning, and ensemble learning), and the challenges and limitations associated with utilizing ML in cancer analysis. The study highlights the potential of ML to improve early detection and patient outcomes while also addressing the technical and practical challenges that must be overcome for its effective clinical integration. Finally, the paper discusses future directions and opportunities for advancing ML applications in cancer research.