SGO-DRE: A Squid Game Optimization-Based Ensemble Method for Accurate and Interpretable Skin Disease Diagnosis

Timely and accurate diagnosis of skin diseases is crucial as conventional methods are time-consuming and prone to errors. Traditional trial-and-error approaches often aggregate multiple models without optimization by resulting in suboptimal performance. To address these challenges, we propose a novel Squid Game Optimization-Dimension Reduction-based Ensemble (SGO-DRE) method for the precise diagnosis of skin diseases. Our approach begins by selecting pre-trained models named MobileNetV1, DenseNet201, and Xception for robust feature extraction. These models are enhanced with dimension reduction blocks to improve efficiency. To tackle the aggregation problem of various models, we leverage the Squid Game Optimization (SGO) algorithm, which iteratively searches for the optimal weightage set to assign the appropriate weightage to each individual model within the proposed weighted average aggregation ensemble approach. The proposed ensemble method effectively utilizes the strengths of each model. We evaluated the proposed method using an 8-class skin disease dataset, a 6-class MSLD dataset, and a 4-class MSID dataset, achieving accuracies of 98.71%, 96.34%, and 93.46%, respectively. Additionally, we employed visual tools like Grad-CAM, ROC curves, and Precision-Recall curves to interpret the decision making of models and assess its performance. These evaluations ensure that the proposed method not only provides robust results but also enhances interpretability and reliability in clinical decision-making.