# Transformative Transparent Hybrid Deep Learning Framework for Accurate Cataract Detection
## 2 Related Works
The study used explainability techniques to localize areas in images that represent disease-relevant areas, enhancing this model for diagnostics in a clinical setting. The results showed not only how well the model is able to identify the diseases themselves, but the areas that feed into such a diagnosis. [PERSON] and [PERSON] [18] conducted a critical review of the literature dealing with AI, machine learning, and deep learning methods related to glaucoma detection. Their work highlighted shortcomings in manual diagnosis while highlighting the benefits of automated systems. Explainable AI, through an adaptive neuro-fuzzy inference system, needs to come forth in order for transparency and interpretability in the decision-making process to be determined. This will greatly enhance user trust in AI and hence has huge potential to improve the prediction and diagnosis of glaucoma. In this direction, [PERSON] et al. [19] have proposed an ensemble model, namely DeB5-XNet, where DenseNet121 and EfficientNetB5 were combined for detecting different ocular diseases: cataracts, diabetic retinopathy, and glaucoma. This model has the CLAHE method, which enhances the features of an image, where it uses Grad-CAM for explainability, and the model predictions are in agreement with clinical observations. It achieved 95% test accuracy with a substantial reduction in false negatives, making it stand out as a reliable tool that ophthalmologists could use. On the technological front, [PERSON] et al. [20] proposed IoT-Opthom-CAD, an IoT-enabled diagnostic system integrated with deep learning by Swin Transformers and LightGBM to achieve multi-disease detection using fundus images. The adoption of Grad-CAM as the explainability tool has empowered the system to visually interpret the features leading to diseases for more informative diagnoses. It had excellent performance, where the reported accuracy was up to 96.5%showing the potential of this system in improving early disease detection in ophthalmology. In addition, [PERSON] et al. [21] designed a LBPSCN for the diagnosis of glaucoma and cataracts. This model addresses the problem of small datasets and, at the same time, illumination variation; hence, it has high accuracy on a number of datasets, reaching as high as 96.87% for cataracts. This will enhance feature extraction and robustness, since this approach promises to serve as an excellent diagnostic tool for ophthalmologists in improving the outcomes of patients. Recently, [PERSON] et al. [22] studied the adoption of an ensemble AI model in age-related macular degeneration diagnosis from OCT images. The paper used a Noor dataset of 16,822 images from 441 patients to provide a comparison of the predictions obtained from individual AI models, namely ResNet, EfficientNet, and Attention models, with the outputs combined by using an ensemble model via trainable weights. The ensemble model yielded the best performance: 91.88% accuracy, 92.54% precision, 92.01% recall, and a 92.03% F1 score in its maiden run, improving further to 97% accuracy after refining misclassified cases. Thisstudy infers the role of AI in improving diagnostic precision for AMD and also illustrates, one step further, the potential that can be released by model fusion while analyzing complex medical images. Thus, the literature indicates a definite trend in innovation in the growing sophistication of CNN-based models for cataract detection. This review underlines the fact that while remarkable progress has been recorded both in terms of accuracy and explainability, further research needs to be carried out on scalability and real-world implementation issues. Further, this review recommends hybrid approaches and interpretable models in order to leverage wider adoption into the clinical practice.