Hybrid Deep Learning Framework for Systemic Lupus Erythematosus Detection Using Xception Convolutional Neural Network with Black-winged Frigate Optimization

Abstract

Systemic Lupus Erythematosus (SLE) is a long-term autoimmune disease where the immune system mistakenly targets and damages healthy tissues and organs. Prevailing models delayed the diagnosis and failed to detect the disease in its early stages, which led to worse patient outcomes and less effective treatments. Therefore, the Xception Convolutional Neural Network with Black-Winged Frigate Optimization Algorithm (XCovNet_Bla-WFOA) is developed for effective SLE detection. First, the input data is collected from the database, and then data normalization is done by Z-score normalization. Besides, relevant features are selected by the proposed Black-Winged Frigate Optimization Algorithm (Bla-WFOA), which fuses the strengths of Magnificent Frigatebird Optimization (MFO) and Black-Winged Kite Algorithm (BKA). Next, data augmentation is accomplished by the Synthetic Minority Over-sampling Technique (SMOTE). Consequently, Lupus Erythematosus detection is implemented by Xception Convolutional Neural Network (XCovNet), where the newly established Bla-WFOA is exploited for optimizing the XCovNet to enhance the detection process. Finally, the Explainable Artificial Intelligence-SHapley Additive exPlanations (XAI-SHAP) are utilized to interpret the detected results. The XCovNet_Bla-WFOA model obtained an accuracy of 93.877%, sensitivity of 95.776%, and specificity of 92.988% for K-value 8.