Precision in Dermatology: Developing an Optimal Feature Selection Framework for Skin Lesion Classification

Tallha Akram; Riaz Junejo; Anas Alsuhaibani; Muhammad Rafiullah; Adeel Akram; Nouf Abdullah Almujally

doi:10.3390/diagnostics13172848

Precision in Dermatology: Developing an Optimal Feature Selection Framework for Skin Lesion Classification

Diagnostics (Basel). 2023 Sep 2;13(17):2848. doi: 10.3390/diagnostics13172848.

Authors

Tallha Akram¹, Riaz Junejo¹, Anas Alsuhaibani², Muhammad Rafiullah³, Adeel Akram¹, Nouf Abdullah Almujally⁴

Affiliations

¹ Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Cantt Campus, Islamabad 45040, Pakistan.
² Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
³ Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan.
⁴ Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.

Abstract

Melanoma is widely recognized as one of the most lethal forms of skin cancer, with its incidence showing an upward trend in recent years. Nonetheless, the timely detection of this malignancy substantially enhances the likelihood of patients' long-term survival. Several computer-based methods have recently been proposed, in the pursuit of diagnosing skin lesions at their early stages. Despite achieving some level of success, there still remains a margin of error that the machine learning community considers to be an unresolved research challenge. The primary objective of this study was to maximize the input feature information by combining multiple deep models in the first phase, and then to avoid noisy and redundant information by downsampling the feature set, using a novel evolutionary feature selection technique, in the second phase. By maintaining the integrity of the original feature space, the proposed idea generated highly discriminant feature information. Recent deep models, including Darknet53, DenseNet201, InceptionV3, and InceptionResNetV2, were employed in our study, for the purpose of feature extraction. Additionally, transfer learning was leveraged, to enhance the performance of our approach. In the subsequent phase, the extracted feature information from the chosen pre-existing models was combined, with the aim of preserving maximum information, prior to undergoing the process of feature selection, using a novel entropy-controlled gray wolf optimization (ECGWO) algorithm. The integration of fusion and selection techniques was employed, initially to incorporate the feature vector with a high level of information and, subsequently, to eliminate redundant and irrelevant feature information. The effectiveness of our concept is supported by an assessment conducted on three benchmark dermoscopic datasets: PH2, ISIC-MSK, and ISIC-UDA. In order to validate the proposed methodology, a comprehensive evaluation was conducted, including a rigorous comparison to established techniques in the field.

Keywords: convolutional neural networks; deep learning; feature fusion; feature selection; gray wolf optimization; skin lesion; transfer learning.

Grants and funding

The funding of this work was provided by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R410), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.