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

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.

An Extensive Investigation into the Use of Machine Learning Tools and Deep Neural Networks for the Recognition of Skin Cancer: Challenges, Future Directions, and a Comprehensive Review

“…We note that 95.1% is the stated accuracy percentage for the current survey in [104]. Using a hybrid technique, Ragab et al [105] have reported precise and accurate diagnosis of the melanoma type of skin cancer. A novel system for classifying lesions was implemented.…”

Segmentation based early Melanoma Detection Using Random Forest Algorithm

“…The paper proposed by M. Ragab et al [33] outlines countermeasures to the limitations of the computerized system, which does not differentiate between healthy skin and melanoma lesions. This is done by declaring a layer-set-based segmentation model that segments inhomogeneous images by examining one hundred separate dermoscopy images.…”

A Review of Recent Advances in Computer-Aided Detection Methods Using Hyperspectral Imaging Engineering to Detect Skin Cancer

“…Different types of skin cancer, including BCC, SCC, SK, and non-epithelial skin cancer, are shown in Figure 1 [114]. Due to the fact that traditional technology does not place an emphasis on spatial and spectral information, a normal eye or smart phone is unable to identify melanoma and BCC in the early stages of skin cancer [115][116][117][118]. Over the course of the last several years, scientists from a wide variety of disciplines have collaborated on the expansion and development of novel dermoscopic technologies for the early diagnosis of skin cancer, as well as the formulation of diagnostic criteria and computer algorithms [119][120][121].…”

“…Notably, the emphasis is on the significance of these findings, highlighting the specific studies that contribute significantly to the meta-analysis and elucidating how different CAD techniques and geographic factors influence skin cancer detection outcomes. This research not only offers insights into the state-of-the-art in skin cancer detection but also Due to the fact that traditional technology does not place an emphasis on spatial and spectral information, a normal eye or smart phone is unable to identify melanoma and BCC in the early stages of skin cancer [115][116][117][118]. Over the course of the last several years, scientists from a wide variety of disciplines have collaborated on the expansion and development of novel dermoscopic technologies for the early diagnosis of skin cancer, as well as the formulation of diagnostic criteria and computer algorithms [119][120][121].…”