Early Detection of Melanoma: Deep Learning and \(m\)-Health for Optimized Patient Outcomes

Authors

  • Md. Moddassir Alam Department of Health Information Management and Technology, College of Applied Medical Sciences, University of Hafr Al Batin, Hafr Al Batin 39524, Saudi Arabia

DOI:

https://doi.org/10.26713/cma.v16i3.2970

Keywords:

m-Health, Melanoma detection, Deep Quantum Neural Network, Type-2 Fuzzy and Cuckoo Search-Based Filter, Stochastic gradient descent

Abstract

Melanoma, an aggressive skin cancer, claims 57,000 lives annually worldwide. Early detection improves survival rates, reduces mortality, enhances treatment outcomes, increases awareness and provides economic benefits. However, detection faces challenges, including limited dermatologist availability, geographical constraints and inaccurate self-assessment. This study proposes an intelligent \(m\)-Health system using Squirrel Search Stochastic Gradient Descent (SSSGD) with Deep Quantum Neural Network (DQNN). The SSSGD is the integration of Squirrel Search Algorithm (SSA) and Stochastic gradient descent (SGD). Here, Type-2 Fuzzy and Cuckoo Search-Based Filter (T2FCS) is used for pre-processing Enhance image quality, resize, and normalize. Spine Generative Adversarial Network (Spine GAN) lesion segmentation, geometric transformations data augmentation, Grey Level Co-occurrence Matrices feature extraction, and DQNN detection trained by SSSGD, providing clinicians with a reliable Decision Support System (DSS) for early diagnosis and effective treatment, enhancing accuracy, computational efficiency, and patient outcomes. Performance evaluation metrics include loss curves, confusion matrix, sensitivity (98.61%), specificity (98.13%) and accuracy (98.42%). Results demonstrate enhanced performance, providing clinicians with a reliable DSS for early diagnosis and effective treatment.

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Published

30-10-2025
CITATION

How to Cite

Alam, M. M. (2025). Early Detection of Melanoma: Deep Learning and \(m\)-Health for Optimized Patient Outcomes. Communications in Mathematics and Applications, 16(3), 777–791. https://doi.org/10.26713/cma.v16i3.2970

Issue

Vol. 16 No. 3 (2025)

Section

Research Article

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