Akram, T.; Junejo, R.; Alsuhaibani, A.; Rafiullah, M.; Akram, A.; Almujally, N.A. Precision in Dermatology: Developing an Optimal Feature Selection Framework for Skin Lesion Classification. Diagnostics2023, 13, 2848.
Akram, T.; Junejo, R.; Alsuhaibani, A.; Rafiullah, M.; Akram, A.; Almujally, N.A. Precision in Dermatology: Developing an Optimal Feature Selection Framework for Skin Lesion Classification. Diagnostics 2023, 13, 2848.
Akram, T.; Junejo, R.; Alsuhaibani, A.; Rafiullah, M.; Akram, A.; Almujally, N.A. Precision in Dermatology: Developing an Optimal Feature Selection Framework for Skin Lesion Classification. Diagnostics2023, 13, 2848.
Akram, T.; Junejo, R.; Alsuhaibani, A.; Rafiullah, M.; Akram, A.; Almujally, N.A. Precision in Dermatology: Developing an Optimal Feature Selection Framework for Skin Lesion Classification. Diagnostics 2023, 13, 2848.
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. This study presents a novel framework for the classification of skin lesions. The framework incorporates deep features to generate a highly discriminant feature vector, while also maintaining the integrity of the original feature space. Recent deep models including Darknet53, DenseNet201, InceptionV3, and InceptionResNetV2 are employed in our study for the purpose of feature extraction. Additionally, transfer learning is leveraged to enhance the performance of our approach. In the subsequent phase, the extracted feature information from the chosen pre-existing models is combined, with the aim of preserving maximum information, prior to undergoing the process of feature selection using a novel entropy-controlled grey wolf optimization (ECGWO) algorithm. The integration of fusion and selection techniques is employed to initially incorporate the feature vector with a high level of information and subsequently eliminate redundant and irrelevant feature information. The efficacy of our design is substantiated through the evaluation on three benchmark dermoscopic datasets, namely PH2, ISIC-MSK, and ISIC-UDA. In order to validate the proposed methodology, a comprehensive evaluation is conducted, including a rigorous comparison with established techniques in the field.
Keywords
convolutional neural networks; feature selection; transfer learning; feature fusion; gray wolf optimization; deep learning; skin lesion
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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