Hacettepe Journal of Mathematics and Statistics 2651-477X 2651-477X Hacettepe University 10.15672/hujms.1821675 Deep Learning Derin Öğrenme SkinToneNet: a robust optimised cascaded multi-scale residual attention network for accurate psoriasis and vitiligo detection across diverse skin types https://orcid.org/0000-0003-3650-8132 Dasari Anantha Reddy Koneru Lakshmaiah Education Foundation https://orcid.org/0000-0001-8026-7888 Shambharkar Saroj Koneru Lakshmaiah Education Foundation https://orcid.org/0000-0001-6417-5414 Lachure Jaykumar Veermata Jijabai Technological Institute https://orcid.org/0000-0003-2445-4747 Damera Vijay Kumar Koneru Lakshmaiah Education Foundation https://orcid.org/0000-0003-2304-5853 Lachure Sagar 03 26 2026 55 2 851 881 11 11 2025 03 09 2026 Copyright © 2002, Hacettepe Journal of Mathematics and Statistics 2002 Hacettepe Journal of Mathematics and Statistics

Accurate detection of chronic skin diseases like Psoriasis and Vitiligo remains challenging due to significant variations in skin pigmentation and lesion presentation across different populations. This paper introduces SkinToneNet, a comprehensive framework designed for robust dermatological diagnosis across diverse skin types. The core methodological contributions include a novel hybrid optimisation algorithm (APVCO) that combines the strengths of Volleyball Premier League and Chimp Optimisation for effective hyperparameter tuning in medical image analysis. Additionally, we propose the CMR-GRU architecture, which cascades Multi-Scale Residual Attention Networks with Gated Recurrent Units to capture both spatial hierarchies and sequential dependencies in skin lesion patterns. The framework integrates optimised segmentation using Adaptive TransUNet with optimised classification via CMR-GRU, both fine-tuned using APVCO. Experimental validation demonstrates that SkinToneNet achieves segmentation Dice scores of 0.894 and IoU of 0.812, with classification accuracy of 95.17% for Psoriasis and 95.19% for Vitiligo across Fitzpatrick skin types I-VI. The system maintains specificity above 93.05% and sensitivity above 93.15% for all skin types, demonstrating consistent performance. The work establishes a methodological foundation for skin-type-agnostic dermatological image analysis while addressing critical challenges in automated diagnosis of Psoriasis and Vitiligo.

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