Title: Advanced deep learning-enabled effective framework for the segmentation and classification of skin disease employing dermatological images

Authors: Priya Jayakanth; G. Rosline Nesa Kumari

Addresses: Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Selaiyur, Chennai, 600127, Tamil Nadu, India ' Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Selaiyur, Chennai, 600127, Tamil Nadu, India

Abstract: A computer-based framework leveraging deep learning was developed for automated skin disease diagnosis, addressing the inaccuracies and inconsistencies of traditional manual methods. The system employs a two-stage process. First, an Adaptive Refined UNetV4 (ARUNetV4) performs disease segmentation by focusing on fine-grained lesion details while suppressing noise. The ARUNetV4's hyperparameters are optimised using the enhanced random variable-based red panda optimisation (ERV-RPO) algorithm. In the second stage, the segmented images are classified using a hybrid Vision Transformer with Residual DenseNet (ViT-RDNet). This model combines ViT's global contextual understanding with RDNet's local feature extraction to overcome visual similarities between different diseases. The framework demonstrated superior performance against existing models, achieving 96% accuracy on Dataset-1 for classification and 95.04% accuracy on Dataset-2 for segmentation.

Keywords: skin disease segmentation and classification; dermatological images; ARUNetV4; Adaptive Refined UNetV4; enhanced red panda optimisation; Residual DenseNet.

DOI: 10.1504/IJAACS.2026.152279

International Journal of Autonomous and Adaptive Communications Systems, 2026 Vol.19 No.1, pp.94 - 121

Received: 25 Apr 2025
Accepted: 15 Sep 2025
Published online: 13 Mar 2026 *

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