# Transformative Transparent Hybrid Deep Learning Framework for Accurate Cataract Detection
## 2 Related Works  
Cataract diagnosis has become one of the fastest-developing medical imaging areas combined with artificial intelligence. This section presents a critical review of the most representative research works, focusing on how CNNs and other deep learning models have evolved in this area. Going by recent studies, traditional methods have increasingly given way to more sophisticated approaches using ResNet, VGG, and DenseNet advanced architectures with a view to boosting the accuracy and robustness of the diagnosis. The review looks into existing literature for pattern identification, gaps, and emerging trends that shape the current state of cataract detection technology. It starts with some early foundational work and moves through some of the more recent advances; it provides broad coverage of different methodologies contributing toward better clinical outcomes as well as explainable AI model development. Later works, building on the earlier findings, such as [PERSON] et al. [13], present a methodology for glaucoma detection using Capsule Networks, referred to as CapsNet, requiring fewer training samples compared to traditional CNNs. The study achieved a promising result of 90.90% accuracy, showing how CapsNet captures the hierarchical variation of the spatial relationships in the images. This is proof that this methodology works well for the early detection of glaucoma with no exhaustive data augmentation and/or optical disk segmentation. Also, [PERSON] [14] introduced a hyper-optimized Vision Transformer model enhanced with explainable AI for detecting and classifying eye diseases from medical images. In the case of the prevalence of eye conditions worldwide, timely diagnosis with precision is key for effective treatment. The current study proposes a new approach based on fine-tuned Vision Transformers, which have been optimized by hyper-parameter tuning in order to enhance both accuracy and generalization. Explainable AI has also been integrated, so the transparency of the model's decision-making process will be provided to allow clinicians to interpret its predictions. It performed best with an accuracy of 91.40%, along with good precision, recall, and F1 score. Moreover, the plausibility provided insight into diagnosis and thus better care. Continuing further in this direction, in 2024, [PERSON] [15], in the year 2024, presented research on applying AlexNet, EfficientNetV2B0, and VGG19 architectures for classifying cataract and glaucoma images using explainable AI techniques such as Grad-CAM for explaining model decisions. It put to work several classification techniques coupled with various optimization strategies for better model performance. It was found that EfficientNetV2B0, combined with 90:10 holdout validation and the RMSProp optimizer, worked best among the different models tested, with an accuracy of 89.77%, showing a potential role of AI in promoting more transparency and diagnostic capabilities within healthcare through explainable AI. [PERSON] et al. [16] focused their work on the glaucoma detection task, exploiting both PCNNs for feature extraction and MLCs for the classification task. For the proposed technique, there is an adoption of explainable AI, enhancing transparency of the model such that users can be informed on how decisions are made. More than 3000 fundus images were considered in this work, with an accuracy as high as 98.03%, good precision, and recall. It allows better trust in the model's predictions with integrated explainability, hence setting the base for future research on automated glaucoma detection. The proposal by [PERSON] et al. [17] made a diagnosis of ocular diseases through automatic analysis of photographs taken from the eye fundus, using convolutional neural networks or CNNs. It involved four experiments on different conditions, such as cataracts and glaucoma, all of which achieved high accuracy.