Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review

Abstract

Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.

Multidirectional Analysis of Curvelet Against Skin Cancer

“…In particular, the suggested method improves the diagnostic performance in relations of classification accurateness. In order to diagnose skin cancer, this project attempts to develop an efficient system for classifying dermoscopic images [52][53][54][55][56][57][58]. A modified MLP is combined with three multi-directional representation systems to create a Hybrid Artificial Intelligence Model (HAIM) for dermoscopic image categorization that successfully accomplishes this goal [59][60][61].…”

Integration of Multimodal Data Sources for Enhanced Skin Disease Classification and Cancer Prediction: A Study Leveraging Pre-Trained Models on HAM_10000 Metadata and Squamous Cell Carcinoma (SCC) Images.

“…In other words, we can say that advanced technology like deep learning is now being used to detect various types of cancer, including brain tumors, breast cancer, lung cancer, esophageal cancer, and skin lesions on the feet. Doctors use imaging methods like dermoscopy [6], CT scans, HRCT scans, and MRI to diagnose cancer and gather information about skin cancer in patients worldwide [7]. To make this technology work, fast internet, powerful computers, and reliable online storage are needed to collect and share skin cancer data.…”

Integration of Multimodal Data Sources for Enhanced Skin Disease Classification and Cancer Prediction: A Study Leveraging Pre-Trained Models on HAM_10000 Metadata and Squamous Cell Carcinoma (SCC) Images.

“…In other words, we can say that advanced technology like deep learning is now being used to detect various types of cancer, including brain tumors, breast cancer, lung cancer, esophageal cancer, and skin lesions on the feet. Doctors use imaging methods like dermoscopy [6], CT scans, HRCT scans, and MRI to diagnose cancer and gather information about skin cancer in patients worldwide [7]. To make this technology work, fast internet, powerful computers, and reliable online storage are needed to collect and share skin cancer data.…”