review · Journal of Imaging
This survey examines the application of deep learning algorithms in the analysis of medical images for various cancers, including breast, cervical, brain, colon, and lung cancers. It notes that deep learning is now a primary approach in medical image analysis. The review found that deep learning has been applied across nearly all imaging modalities for cervical and breast cancers, and MRIs for brain tumours. These methods have achieved state-of-the-art results in tasks such as tumour detection, segmentation, feature extraction, and classification. The survey identifies three main deep learning approaches: training models from scratch, using transfer learning by freezing network layers, and modifying architectures to reduce parameters. It also highlights that while research is extensive in economically developed countries, it has received less attention in Africa despite rising cancer risks.
This research highlights the significant potential of deep learning to enhance the accuracy and efficiency of cancer diagnosis from medical images. Improved detection and classification can lead to earlier interventions and better patient outcomes. The identified disparity in research focus also points to an opportunity for increased technological development in Africa.
The reviewed deep learning methods offer advanced capabilities for automated or semi-automated cancer detection, segmentation, and classification from medical scans. These could be integrated into diagnostic tools for radiologists and clinicians, potentially improving workflow efficiency and diagnostic accuracy. As a survey of existing state-of-the-art applications, the underlying technologies appear mature for further development into clinical products, particularly in regions where such applications are less explored.
AI-generated from the published abstract. Always read the original work before citing.
Deep learning algorithms have become the first choice as an approach to medical image analysis, face recognition, and emotion recognition. In this survey, several deep-learning-based approaches applied to breast cancer, cervical cancer, brain tumor, colon and lung cancers are studied and reviewed. Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification. As presented in this paper, the deep learning approaches were used in three different modes that include training from scratch, transfer learning through freezing some layers of the deep learning network and modifying the architecture to reduce the number of parameters existing in the network. Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed countries; while, the study has not had much attention in Africa despite the dramatic soar of cancer risks in the continent.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.3390/jimaging6110121
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
