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Classification of Skin Cancer Lesions Using Explainable Deep Learning

Zia Ur Rehman, Muhammad; Ahmed, Fawad; Alsuhibany, Suliman A.; Jamal, Sajjad Shaukat; Zulfiqar Ali, Muhammad; Ahmad, Jawad

Authors

Muhammad Zia Ur Rehman

Fawad Ahmed

Suliman A. Alsuhibany

Sajjad Shaukat Jamal

Muhammad Zulfiqar Ali



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.

Journal Article Type Article
Acceptance Date Sep 7, 2022
Online Publication Date Sep 13, 2022
Publication Date 2022
Deposit Date Sep 28, 2022
Publicly Available Date Sep 28, 2022
Journal Sensors
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 22
Issue 18
Article Number 6915
DOI https://doi.org/10.3390/s22186915
Keywords classification; deep learning; explainable AI (XAI); skin cancer; transfer learning
Public URL http://researchrepository.napier.ac.uk/Output/2917329

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