# Transformative Transparent Hybrid Deep Learning Framework for Accurate Cataract Detection
## 3 Materials and Methods
### Methods
#### 3.2.2 The Model
Precision is generally defined as the reliability of the model in predicting cataract cases. The score of 1.0 depicts no false positive. The recall showed that the model has measured all cataract cases. An F1 score of 1.0 shows that precision and recall have the best balance, hence the optimum performance of the model with a minimum error in the medical diagnosis as shown in Figure 4.

To further evaluate the performance of the hybrid model, a confusion matrix was generated, as illustrated in Figure 5 This matrix provides a comprehensive overview of the classification model's ability to distinguish between eye images categorized as cataract and normal. As depicted in Figure 5, the rows in the confusion matrix correspond to the true classes, while the columns represent the predicted classes generated by the model. The results indicate that the model accurately identified 61 instances of cataract images, showcasing its effectiveness in recognizing this condition. Additionally, it correctly classified 60 instances as normal, underscoring its robust performance in identifying healthy eye images. Importantly, the model demonstrated no false positives, meaning it did not misclassify any normal images as cataract cases. Likewise, there were no false negatives, signifying that the model did not overlook any instances of cataract. This performance level reflects impressive accuracy across both classes. The metrics derived from the confusion matrix suggest that the model possesses an exceptional ability to differentiate between cataract and normal eye images, highlighting its reliability and effectiveness in clinical settings. Figure 4: The model’s performance metrics. Figure 5: Confusion matrix for the cataract detection model. To further evaluate the performance of the hybrid model, the Receiver Operating Characteristic (ROC) curve was analyzed, as shown in Figure 3:0 This curve is essential for assessing the model's ability to distinguish between cataract and normal eye images by illustrating the relationship between the True Positive Rate (TPR) and the False Positive Rate (FPR) across various threshold settings. The TPR indicates the proportion of actual cataract cases correctly identified by the model, while the FPR reflects the proportion of normal cases misclassified as positives. The ROC curve's proximity to the upper left corner demonstrates the model's high sensitivity and low false positive rate. Notably, the model achieved a perfect area under the ROC curve (AUC) of 1.00, indicating exceptional classification capability and a strong ability to differentiate between the two classes. For further performance evaluation of the hybrid model, both training and validation loss and accuracy are presented with great care, as shown in the following plots as depicted in Figure 3:1 On the left, it shows a plot of training and validation loss across the epochs, where both are consistently decreasing. That means this model is learning well, the training loss decreases with the increase in the number of epochs, and it also shows that the trend in the validation loss has a similar shape, which suggests that the generalization capability of the model does not become hampered due to overfitting. The right plot depicts the model's accuracy for both training and validation phases. While both training and validation accuracy steadily increased, the former reached an almost 100% value toward the end of epochs. The latter also showed strong improvement, with values closing in on the same heights, thereby proving the model's strength for differentiating cataract images from normal ones. These results are particularly important in the context of the previously discussed confusion matrix and ROC curve analyses, which already confirmed the correctness of eye image classification by this model. The convergence of training and validation accuracy, together with a small gap between their values, points to the strong predictive performance of the model and further potential use in clinical applications.