Supervised Versus Unsupervised Deep Learning Based Methods for Skin Lesion Segmentation in Dermoscopy Images

Image segmentation is considered a crucial step in automatic dermoscopic image analysis as it affects the accuracy of subsequent steps. The huge progress in deep learning has recently revolutionized the image recognition and computer vision domains. In this paper, we compare a supervised deep learning based approach with an unsupervised deep learning based approach for the task of skin lesion segmentation in dermoscopy images. Results show that, by using the default parameter settings and network configurations proposed in the original approaches, although the unsupervised approach could detect fine structures of skin lesions in some occasions, the supervised approach shows much higher accuracy in terms of Dice coefficient and Jaccard index compared to the unsupervised approach, resulting in 77.7% vs. 40% and 67.2% vs. 30.4%, respectively. With a proposed modification to the unsupervised approach, the Dice and Jaccard values improved to 54.3% and 44%, respectively

A survey, review, and future trends of skin lesion segmentation and classification

The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis

Human Face Mapping Based on TEWL, Hydration and Ultrasound

Biophysical properties of the skin vary depending on the skin location. Such properties include skin structure, density of skin layers, pH, temperature, hydration and Transepidermal Water Loss (TEWL).Modern technologies and quantitative methods allow reading and analysing the skin properties using in-vivo based analysis. One goal of such analysis is partitioning the skin in areas with similar properties, which is referred as mapping. The purpose of our study, also the novelty of the project, is mapping of the facial skin in terms of TEWL, hydration and skin layer thickness, as well as measuring the effect of physical exercise on facial skin; where possible, effect of sex and age were also considered. TEWL was measured with AquaFlux, skin layer thickness was measured with Episcan high resolution ultrasound imaging, and skin hydration was measured with Epsilon. Our study reveals material difference of TEWL between the facial sites being analysed; the largest differences were noted between the lips and the neck. It was found that skin hydration levels decrease with the advancement of age. Skin hydration readings reveal larger general effect of exercise for females, and strongest effect for males observed on the nose. Skin ultrasound images were used in two ways. First, face was mapped in terms of the thickness of the individual skin layers and such mapping was found to be different for each layer. Secondly, the differences between the sites in terms of thickness were quantified using Welch test, where age was also found to be a factor. Several Machine Learning-based classifiers of the skin location were also trained, which are based on the cross-sectional image with moderate positive outcome. The study showed that the combination of TEWL, Epsilon and Episcan provides useful information about skin health. The study also showed variations in the values for different facial skin sites of several skin samples, which was likely due to the degree of corneocyte formation, the lipid contents of the Stratum Corneum (SC), skin temperature, damaged barrier function, bodily health and skin blood flow