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Adaptive self-learning is a promising technique in medical image analysis that enables deep learning models to adapt to changes in image distribution over time. As medical image data can vary due to factors like imaging equipment and patient demographics, adaptive self-learning becomes valuable for maintaining the accuracy and robustness of deep learning frameworks. Initially trained on a large dataset, the framework can adapt to new modalities using transfer learning, adaptive learning, and incremental learning, incorporating both manual and auto (CNN-based) features. Adaptive self-learning offers various benefits, including improved model accuracy and efficiency, reducing the need for manual retraining. However, challenges such as the risk of overfitting, acquiring relevant manual features, and careful monitoring need to be addressed. Combining manual features and pretrained CNN models can enhance …