Abstract: Deep learning, in particular Convolutional Neural Networks (CNNs), have begun to serve as a great asset for improving many aspects in oncology including cancer detection, diagnosis, and treatment. This survey paper presents an overview of the works that employed CNN-based techniques towards the early detection of different cancers i.e. breast, lung, prostate and skin cancer. We investigate the application of CNN on medical image processing, primarily for radiographic imaging, including CTs, MRIs, and histopathological sections. Paper considers the actual studies devoted to the development of new CNN architectures, image preprocessing techniques, and transfer learning approaches aimed at increasing the cancer detection systems accuracy and efficiency.
Nevertheless, a number of issues still need to be resolved, such as the high expense of acquiring high-quality data, the inability of deep learning models to be interpreted, and the requirement for big annotated datasets. Additionally, the survey article shows how CNNs could be used to increase the accuracy of cancer diagnosis when combined with other machine learning and imaging methods like multimodal imaging and genomics. Finally, the survey discusses the direction of subsequent research in the use of CNNs in oncology, including applying clinical workflows, diagnostics, and precision medicine in all its aspects.
Keywords: Deep - learning, Oncology, CNN Architectures, Classification, Cancer Detection, pre trained CNN Network
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DOI:
10.17148/IJARCCE.2025.14337
