An Explainable AI and Optimized Multi-Branch Convolutional Neural Network Model for Eye Anemia Diagnosis

This paper proposes a novel, non-invasive approach to diagnosing eye anemia using deep learning techniques. Traditional methods, reliant on invasive procedures like venipuncture, are costly and can cause patient discomfort. Our model leverages a multi-branch convolutional neural network (CNN) architecture, incorporating the Hippopotamus Optimization (HO) algorithm and multiclass support vector machines (SVMs) for enhanced accuracy. To address data imbalance, we employ the Synthetic Minority Oversampling Technique (SMOTE) and data augmentation. The model is trained and evaluated on a dataset of 211 eye images. The model achieves a remarkable 97.06% accuracy, with a Receiver Operating Characteristic (ROC) curve demonstrating an Area Under the Curve (AUC) of 0.973, indicating strong discriminative power. The parallel branch CNN architecture significantly improves training speed and reduces inference time. Furthermore, t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization effectively clusters data points, showcasing the model’s ability to distinguish between anemic and non-anemic cases. To ensure model transparency and reliability, we utilize the SHapley Additive exPlanations (SHAP) method to understand feature importance. This non-invasive approach holds significant promise for early and efficient anemia detection, particularly in resource-constrained settings.