# Transformative Transparent Hybrid Deep Learning Framework for Accurate Cataract Detection
## 3 Materials and Methods
### Methods
#### 3.2.2 The Model
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Comparison** & **KL Divergence** & **Interpretation** \\ \hline Heatmap2 (VGG16 + Siamese) & & The lowest divergence recorded, showing a close alignment in focus between the two configurations with significant feature similarity. \\  vs. Heatmap3 (VGG16 + Siamese + LRS) & & Suggests notable similarities but also distinct differences, implying that the configurations captured different aspects of the input images. \\ Heatmap3 (VGG16 + Siamese + LRS) & & Low divergence, confirming a close alignment between Heatmap3 and Heatmap2, reinforcing that they produced similar interpretations of the input data. \\  vs. Heatmap2 (VGG16 + Siamese + LRS) & & Heatmap2, reinforcing that they produced similar interpretations of the input data. \\ + Siamese & & \\ + Siamese & & \\ + Siamese & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Author(s)** & **Model/Methodology** & **Disease Focus** & **Accuracy** & **Explainability Tool** \\ \hline [PERSON] et al. [13] & Capsule Networks (CapsNet) & Glaucoma & 90.90\% & None \\  [PERSON], [14] & Vision Transformer (ViT) with Explainable AI & Eye diseases (general) & 91.40\% & Explainable AI \\ [PERSON], [15] & AlexNet, EfficientNetV2B0, VGG19 with Grad-CAM & Cataract, Glaucoma & 89.77\% & Grad-CAM \\  [PERSON] et al. [16] & PCNNs for feature extraction & Glaucoma & 98.03\% & Explainable AI \\  [PERSON] et al. [17] & CNNs for automatic analysis of ocular diseases & Cataract, Glaucoma & 95\% & Explainability \\  [PERSON] et al. [18] & EfficientNetS9 with CLAHE and Grad-CAM & Cataract, Diabetic Retinopathy & 95\% & Grad-CAM \\  [PERSON] et al. [19] & IoT-Ophom-CAD (Swin & & \\ \multicolumn{1}{c}{Transformers + LightGBM)} & \multicolumn{1}{c}{Multi-disease detection} & \multicolumn{1}{c}{96.5\%} & Grad-CAM \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Table of comparison. The comparison analysis shows that different deep learning methodologies have been applied to detect ocular diseases, each with its own distinct advantages in accuracy and explainability, as shown in Figure 8. Models like [PERSON] et al. 's [16] and [PERSON] et al. 's [21] ensemble model show high accuracy, while some lack reported performance metrics. The proposed hybrid deep learning model, which combines VGG16 and a Siamese network with a learning rate scheduler, achieves superior accuracy (100%) and incorporates the Grad-CAM explainability tool. This positions the proposed model as a state-of-the-art solution in cataract detection, excelling in both accuracy and the transparency of decision-making, making it a valuable asset in medical diagnostics. ## 5 Discussion

Figure 10 presents the results of the Grad-CAM analysis, providing insight into the interpretability of deep learning models, specifically the Siamese network and VGG16 architecture. Grad-CAM generates a coarse localization map that highlights critical image regions influencing the model's predictions. By utilizing the gradients from the final convolutional layers, the heat map displays varying importance levels across the image, indicated by a color gradient ranging from red (high importance) to blue (low importance). \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Author(s)** & **Model/Methodology** & **Disease Focus** & **Accuracy** & **Explainability Tool** \\ \hline [PERSON] et al. [20] & LBPSCN for small datasets with illumination variation & Cataract & 96.87\% & None \\  & Ensemble model (ResNet, & & & \\  & EfficientNet, Attention models) & & & \\ [PERSON] et al. [21] & for age-related macular & AMD & 97\% & None \\  & degeneration from OCT & & & \\  & Higgs & Hybrid deep learning(VGG16 & & \\  & and Siamese Network with learning rate scheduler) & Cataract & 100\% & Grad-CAM \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._

Figure 9: Comparison of model accuracy [13; 14; 15; 16; 17; 18; 19; 20; 21].