Open Access

ARTICLE

BioSkinNet: A Bio-Inspired Feature-Selection Framework for Skin Lesion Classification

Tallha Akram^1,*, Fahdah Almarshad¹, Anas Alsuhaibani¹, Syed Rameez Naqvi^2,3

1 Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
2 Department of Electrical and Computer Engineering, COMSATS Univeristy Islamabad, Wah Campus, WahCantt, 47010, Pakistan
3 Department of Computer Science, Tulane University, New Orleans, LA 70118, USA

* Corresponding Author: Tallha Akram. Email:

(This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)

Computer Modeling in Engineering & Sciences 2025, 143(2), 2333-2359. https://doi.org/10.32604/cmes.2025.064079

Received 04 February 2025; Accepted 08 April 2025; Issue published 30 May 2025

Abstract

Melanoma is the deadliest form of skin cancer, with an increasing incidence over recent years. Over the past decade, researchers have recognized the potential of computer vision algorithms to aid in the early diagnosis of melanoma. As a result, a number of works have been dedicated to developing efficient machine learning models for its accurate classification; still, there remains a large window for improvement necessitating further research efforts. Limitations of the existing methods include lower accuracy and high computational complexity, which may be addressed by identifying and selecting the most discriminative features to improve classification accuracy. In this work, we apply transfer learning to a Nasnet-Mobile CNN model to extract deep features and augment it with a novel nature-inspired feature selection algorithm called Mutated Binary Artificial Bee Colony. The selected features are fed to multiple classifiers for final classification. We use , ISIC-2016, and HAM datasets for experimentation, supported by Monte Carlo simulations for thoroughly evaluating the proposed feature selection mechanism. We carry out a detailed comparison with various benchmark works in terms of convergence rate, accuracy histogram, and reduction percentage histogram, where our method reports 99.15% (2-class) and 97.5% (3-class) accuracy on the dataset, while 96.12% and 94.1% accuracy for the other two datasets, respectively, against minimal features.

---

Keywords

---