Skin Cancer Diagnosis Based on Neutrosophic Features with a Deep Neural Network

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-08, Vol.22 (16), p.6261
• Main Authors: Singh, Sumit Kumar, Abolghasemi, Vahid, Anisi, Mohammad Hossein
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Cancer; Classification; Datasets; deep neural network; Dermoscopy - methods; Diagnosis; Digital imaging; Hair; Histograms; Humans; Image acquisition; Image classification; Image enhancement; image processing; Image Processing, Computer-Assisted - methods; Medical diagnosis; Medical imaging; Melanoma; Melanoma - diagnosis; Model accuracy; Neural networks; Neural Networks, Computer; neutrosophic; Partial differential equations; Skin cancer; Skin Neoplasms - diagnosis; Skin Neoplasms - pathology
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22166261

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.