FISM: harnessing deep learning and reinforcement learning for precision detection of microaneurysms and retinal...

Diabetic retinopathy (DR) is a primary cause of blindness globally and its treatment and management depend on accurate and timely identification. Current approaches for DR detection and segmentation repeatedly fall short in accuracy and sturdiness highlighting the essential for advanced computational methods. In this study propose a deep learning model Fundus Images Segmentation Model (FISM) designed to precisely detect microaneurysms and retinal exudates dangerous indicators of DR. Employing the Diabetic Retinopathy Dataset (DDR), our model utilizes both the segmentation and grading subsets, comprising over 13,000 fundus images annotated with comprehensive lesion-level and DR severity information, enabling robust training for both detection and classification tasks. The preprocessing pipeline contains band separation generative adversarial network (GAN) based data augmentation and extensive normalization techniques. The FISM architecture is derived from the Segment Anything Model (SAM) exclusively integrating transformer layers and patch embedding techniques. The model begins with patch embedding followed by transformer blocks to capture both local and global relationships within retinal images. The architecture employs transfer learning, domain-specific fine-tuning customized loss functions and attention mechanisms to optimize feature extraction and segmentation accuracy. The image encoder and Mask decoder modules work in tandem to transform input retinal images into precise segmentation Masks, highlighting regions affected by DR. Beyond deep learning, the framework also integrates reinforcement learning to constructively direct the exploration of regions of interest so that the model is capable of highlighting areas of interest to a diagnosis. This form of adaptive attention is an improvement in the precision of detection and computational cost. Results show that FISM outperforms state-of-the-art methods, achieving 96.32% accuracy, 95.14% precision, 95.25% recall and a 96.33% F1-score. The model demonstrates an AUC of 96.32%, specificity of 94.13%, segmentation Dice coefficient of 94.21% and IoU of 96.01%. These metrics indicate superior performance in both detection and segmentation tasks for early diabetic retinopathy diagnosis.