Singh, Sumit Kumar and Abolghasemi, Vahid and Anisi, Mohammad Hossein (2022) Skin Cancer Diagnosis Based on Neutrosophic Features with a Deep Neural Network. Sensors, 22 (16). p. 6261. DOI https://doi.org/10.3390/s22166261
Singh, Sumit Kumar and Abolghasemi, Vahid and Anisi, Mohammad Hossein (2022) Skin Cancer Diagnosis Based on Neutrosophic Features with a Deep Neural Network. Sensors, 22 (16). p. 6261. DOI https://doi.org/10.3390/s22166261
Singh, Sumit Kumar and Abolghasemi, Vahid and Anisi, Mohammad Hossein (2022) Skin Cancer Diagnosis Based on Neutrosophic Features with a Deep Neural Network. Sensors, 22 (16). p. 6261. DOI https://doi.org/10.3390/s22166261
Abstract
Recent years evidenced an increase in the total number of skin cancer cases, and it is projected to grow exponentially. This paper proposes a computer-aided diagnosis system for the classification of a malignant lesion, where the acquired image is primarily pre-processed using novel methods. Digital artifacts such as hair follicles and blood vessels are removed, and thereafter, the image is enhanced using a novel method of histogram equalization. Henceforth, the pre-processed image undergoes the segmentation phase, where the suspected lesion is segmented using the Neutrosophic technique. The segmentation method employs a thresholding-based method along with a pentagonal neutrosophic structure to form a segmentation mask of the suspected skin lesion. The paper proposes a deep neural network base on Inception and residual blocks with softmax block after each residual block which makes the layer wider and easier to learn the key features more quickly. The proposed classifier was trained, tested, and validated over PH2, ISIC 2017, ISIC 2018, and ISIC 2019 datasets. The proposed segmentation model yields an accuracy mark of 99.50%, 99.33%, 98.56% and 98.04% for these datasets, respectively. These datasets are augmented to form a total of 103,554 images for training, which make the classifier produce enhanced classification results. Our experimental results confirm that the proposed classifier yields an accuracy score of 99.50%, 99.33%, 98.56%, and 98.04% for PH2, ISIC 2017, 2018, and 2019, respectively, which is better than most of the pre-existing classifiers.
Item Type: | Article |
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Uncontrolled Keywords: | melanoma; neutrosophic; image processing; deep neural network |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 30 Aug 2022 11:13 |
Last Modified: | 30 Oct 2024 15:48 |
URI: | http://repository.essex.ac.uk/id/eprint/33342 |
Available files
Filename: Skin Cancer Diagnosis Based on Neutrosophic Features with a Deep Neural Network.pdf
Licence: Creative Commons: Attribution 3.0