Early and accurate detection of melanoma skin cancer using hybrid level set approach

Mahmoud Ragab; Hani Choudhry; Mohammed W Al-Rabia; Sami Saeed Binyamin; Ahmed A Aldarmahi; Romany F Mansour

doi:10.3389/fphys.2022.965630

Early and accurate detection of melanoma skin cancer using hybrid level set approach

Front Physiol. 2022 Dec 5:13:965630. doi: 10.3389/fphys.2022.965630. eCollection 2022.

Authors

Mahmoud Ragab^{1

2

3}, Hani Choudhry^{2

4}, Mohammed W Al-Rabia^{5

6}, Sami Saeed Binyamin⁷, Ahmed A Aldarmahi^{8

9}, Romany F Mansour¹⁰

Affiliations

¹ Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
² Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia.
³ Mathematics Department, Faculty of Science, Al-Azhar University, Nasr City, Egypt.
⁴ Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.
⁵ Department of Medical Microbiology and Parasitology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.
⁶ Health Promotion Center, King Abdulaziz University, Jeddah, Saudi Arabia.
⁷ Computer and Information Technology Department, The Applied College, King Abdulaziz University, Jeddah, Saudi Arabia.
⁸ Basic Science Department, College of Science and Health Professions, King Saud Bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia.
⁹ King Abdullah International Medical Research Center, Ministry of National Guard-Health Affairs, Jeddah, Saudi Arabia.
¹⁰ Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, Egypt.

Abstract

Digital dermoscopy is used to identify cancer in skin lesions, and sun exposure is one of the leading causes of melanoma. It is crucial to distinguish between healthy skin and malignant lesions when using computerised lesion detection and classification. Lesion segmentation influences categorization accuracy and precision. This study introduces a novel way of classifying lesions. Hair filters, gel, bubbles, and specular reflection are all options. An improved levelling method is employed in an innovative method for detecting and removing cancerous hairs. The lesion is distinguished from the surrounding skin by the adaptive sigmoidal function; this function considers the severity of localised lesions. An improved technique for identifying a lesion from surrounding tissue is proposed in the article, followed by a classifier and available features that resulted in 94.40% accuracy and 93% success. According to research, the best method for selecting features and classifications can produce more accurate predictions before and during treatment. When the recommended strategy is put to the test using the Melanoma Skin Cancer Dataset, the recommended technique outperforms the alternative.

Keywords: cancer; computer aided design; dermoscopy; lesion segmentation; level set; melanoma; skin lesions.