Classification of skin cancer lesions using explainable deep learning

Zia Ur Rehman, M., Ahmed, F., Alshuhibany, S. A., Jamal, S. S., Ali, M. Z. and Ahmad, J. (2022) Classification of skin cancer lesions using explainable deep learning. Sensors, 22(18), 6915. (doi: 10.3390/s22186915) (PMID:36146271) (PMCID:PMC9505745)

Abstract

Skin cancer is among the most prevalent and life-threatening forms of cancer that occur worldwide. Traditional methods of skin cancer detection need an in-depth physical examination by a medical professional, which is time-consuming in some cases. Recently, computer-aided medical diagnostic systems have gained popularity due to their effectiveness and efficiency. These systems can assist dermatologists in the early detection of skin cancer, which can be lifesaving. In this paper, the pre-trained MobileNetV2 and DenseNet201 deep learning models are modified by adding additional convolution layers to effectively detect skin cancer. Specifically, for both models, the modification includes stacking three convolutional layers at the end of both the models. A thorough comparison proves that the modified models show their superiority over the original pre-trained MobileNetV2 and DenseNet201 models. The proposed method can detect both benign and malignant classes. The results indicate that the proposed Modified DenseNet201 model achieves 95.50% accuracy and state-of-the-art performance when compared with other techniques present in the literature. In addition, the sensitivity and specificity of the Modified DenseNet201 model are 93.96% and 97.03%, respectively.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ali, Dr Muhammad

Creator Roles

Ali, M. Z.: Writing – review and editing

• Authors: Zia Ur Rehman, M., Ahmed, F., Alshuhibany, S. A., Jamal, S. S., Ali, M. Z., and Ahmad, J.
• College/School: College of Science and Engineering > School of Engineering
• Journal Name: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• ISSN (Online): 1424-8220
• Published Online: 13 September 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Sensors 22(18): 6915
• Publisher Policy: Reproduced under a Creative Commons License

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