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@article{201668,
        author = {Mohammed Huzaifa Ansari},
        title = {Deep Learning for Medical Image Analysis: A Survey},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {10060-10066},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201668},
        abstract = {Over the last several years, deep learning (DL) techniques have impacted how medical image analysis occurs by providing automated, accurate and scalable methods to detect disease, segment images and diagnose patients. This review synthesizes the development of convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN) and transformer based architectures in relation to their use in medical image analysis. Each study included here includes a complete description of the technologies utilized for each of the studies reviewed, details of the data sets used and the resulting metrics. The review also includes the clinical implications of these studies. The review has identified several barriers preventing the implementation of DL within the medical community including limited access to training data sets, lack of interpretability and transferability of trained models from one domain to another. Nevertheless, the review has identified many potential areas for future development including multimodal learning and self-supervised learning frameworks. Overall, as deep learning continues to develop, we expect improvements in the accuracy, efficiency and ultimately quality of healthcare delivery which will result from the application of DL to diagnostic information generation and increased automation of healthcare workloads.},
        keywords = {},
        month = {May},
        }