Open Access

ARTICLE

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

Areeba Masood Siddiqui^1,2,*, Hyder Abbas^3,4, Muhammad Asim^5,6,*, Abdelhamied A. Ateya⁵, Hanaa A. Abdallah⁷

1 Department of Science in Engineering Management, Trine University, Detroit, MI 48101-3636, USA
2 Department of Biomedical Engineering, Sir Syed University of Engineering and Technology (SSUET), Karachi, 75300, Pakistan
3 School of Mathematics and Statistics, Guizhou University, Guiyang, 550025, China
4 State Key Laboratory of Public Big Data, College of Computer Science and Technology, Institute for Artificial Intelligence, Guizhou University, Guiyang, 550025, China
5 EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia
6 School of Electronic Information, Central South University, Changsha, 410083, China
7 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

* Corresponding Authors: Areeba Masood Siddiqui. Email: ; Muhammad Asim. Email:

Computer Modeling in Engineering & Sciences 2025, 144(3), 3135-3168. https://doi.org/10.32604/cmes.2025.069926

Received 03 July 2025; Accepted 11 August 2025; Issue published 30 September 2025

Abstract

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.

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Keywords

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