Deep Learning Approaches for Small Data Sets in Medical Imaging
DOI:
https://doi.org/10.61841/fc5kap18Keywords:
Deep learning, small data sets, medical imaging, transfer learning, data augmentationAbstract
Clinical imaging assumes a urgent part in conclusion and treatment arranging, with headways in innovation persistently upgrading its capacities. In any case, the shortage of marked information stays a huge test, hindering the organization of profound learning models, which frequently require enormous datasets for successful preparation. This examination article investigates inventive ways to deal with address the restrictions forced by little clinical imaging datasets, utilizing the force of profound learning strategies. The review starts by featuring the basic significance of precise and dependable clinical picture examination with regards to restricted information accessibility. We dig into the extraordinary qualities of clinical imaging datasets, for example, the high dimensionality of pictures and the complexities of obsessive varieties, which require custom-made answers for powerful model preparation. Customary profound learning models battle with little datasets, prompting overfitting and less than ideal speculation. Accordingly, our examination explores novel techniques, including move learning, information expansion, and group strategies, to upgrade model execution with restricted named tests. Move learning arises as a key concentration, saddling the information procured from pre-prepared models for huge scope datasets and adjusting it to the particular subtleties of clinical imaging. We investigate the adaptability of elements gained from different spaces and explore their materialness to clinical picture examination. Moreover, the article talks about the job of information expansion in misleadingly growing the dataset, improving model strength and speculation abilities. In addition, the study introduces ensemble methods as a means of mitigating the dangers posed by small datasets and taking advantage of the variety of model architectures. In medical imaging tasks, we aim to improve overall performance and increase model reliability by combining predictions from multiple models.
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