Work

Hybrid explainable deep learning framework for skin disease detection and future risk prediction using Sakaguchi function with CNN-transformer models

Year: 2025

Type: article

Abstract: Skin disorders, which include a variety of bacterial, fungal, viral, and parasitic illnesses, represent a major global health concern. Using dermatoscopic pictures, this study suggests an intelligent and comprehensible deep learning-based method for automated skin disease detection and risk prediction. Hair removal, bilateral filtering, K-means clustering for artifact suppression, and histogram equalization to improve lesion clarity are all part of the preprocessing procedure. In addition to this, the Sakaguchi function, mathematically expressed as f ( x ) = x · exp ( − α x 2 ) where α is a tunable contrast-control parameter, is introduced as an advanced enhancement technique. Unlike conventional methods such as CLAHE, it adaptively enhances local contrast while preserving edges and minimizing noise amplification, thereby improving lesion texture visibility and segmentation accuracy. Features were extracted using pretrained CNN models like ResNet50 and InceptionV3, followed by hybrid deep learning architectures such as CNN-BiLSTM, CNN-Capsule Networks, and CNN combined with Vision Transformers (ViT). With an accuracy of 96.10 %, precision of 96.40 %, recall of 95.80 %, and F1-score of 96.10 %, the CNN + ViT model combined with Sakaguchi-based preprocessing produced the best results. Clinical dependability was improved by using explainable AI approaches as Grad-CAM, Layer-wise Relevance Propagation (LRP), and SHAP to visualize and analyze model decisions. GANs were used to generate synthetic data that enhanced performance on minority classes in order to remedy class imbalance. The Overall, the CNN-ViT hybrid framework’s incorporation of the mathematically defined Sakaguchi enhancement shows theoretical and practical improvements in generalization, interpretability, and robustness, making it appropriate for dermatological diagnosis and progression monitoring in the real world.

Source: Results in Engineering

DOI: 10.1016/j.rineng.2025.108489

Authors: Pavani Maram, Parvathi Ramasubramanian, Pattabiraman Venkatasubbu

Institution: Vellore Institute of Technology University

Language: English

Topic: Cutaneous Melanoma Detection and Management

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,940