A fast interpretable adaptive meta-learning enhanced deep learning framework for diagnosis of diabetic retinopathy

•An new inner hyperparameter generation network based on logistic regression.•Increased interpretability of inner loops in the existing methods.•A new gradual hyperparameter adjustment strategy.•Greatly improves the speed of the algorithm in fitting new tasks. Gradient-based meta-learning algorithms offer promising solutions to the challenge of swift adaptation to new tasks, especially when faced with limited sample data. One pivotal concern in few-shot classification tasks is striking the right balance between interpretability and accuracy within the meta-learning framework. This study introduces a novel methodology titled Fast, Interpretable, and Adaptive meta-Learning based on Logistic Regression (FIAML-LR). Distinctively, FIAML-LR employs logistic regression to craft a meta-network in the inner loop. This design facilitates faster generation of the learning rate and weight attenuation coefficient, enhancing the interpretability of meta-learning for new task adaptation. An adaptable parameter update strategy is also embedded, initializing with a broader hyperparameter adjustment scope and fine-tuning progressively throughout the experiment. Experimental evidence reveals that, when implemented on a 4-CONV architecture, FIAML-LR not only bolsters the model's interpretability but also amplifies its accuracy for few-shot classification tasks. A focused investigation on the diabetic retinopathy dataset demonstrated that FIAML-LR, even with limited data, could boost classification accuracy by a significant 14.28% against the benchmark model. This heightened accuracy could aid medical professionals in more precisely diagnosing diabetic retinopathy.