Transfer Learning in Biomedical Image Classification

Transfer learning has emerged as a highly effective method for classifying biomedical images, as it entails the use of pre-trained neural networks on large and diverse datasets. The efficacy of models is substantially enhanced by this method. The challenges associated with training deep learning models from inception, such as the limited availability of annotated data and high computational costs, are circumvented by this strategy. Transfer learning expedites the training process by enhancing the precision and applicability of pre-trained models through the use of domain-specific biological imagery. By examining the various applications and techniques of transfer learning in biomedical imaging, this study investigates its potential future, drawbacks, and benefits.

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