Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet

Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed visually employing clinical screening, a biopsy, dermoscopic analysis, and histopathological examination. It has been demonstrated that the dermoscopic analysis in the hands of inexperienced dermatologists may cause a reduction in diagnostic accuracy. Early detection and screening of skin cancer have the potential to reduce mortality and morbidity. Previous studies have shown Deep Learning ability to perform better than human experts in several visual recognition tasks. In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists. We used a pretrained MobileNet model to train over HAM10000 dataset using transfer learning. The model classifies skin lesion image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36 percent and top3 accuracy of 95.34 percent. The weighted average of precision, recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The model has been deployed as a web application for public use at (https://saketchaturvedi.github.io). This fast, expansible method holds the potential for substantial clinical impact, including broadening the scope of primary care practice and augmenting clinical decision-making for dermatology specialists.Comment: This is a pre-copyedited version of a contribution published in Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R., Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The definitive authentication version is available online via https://doi.org/10.1007/978-981-15-3383-9_1

Melanosis of the lower lip subverted by filler injection: a simulator of early mucosal melanoma

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Reflectance confocal microscopy for the diagnosis of skin infections and infestations

Reflectance confocal microscopy (RCM) is a noninvasive real-time imaging technique that has been widely used for the diagnosis of skin cancer. More recently, it has been reported as a useful tool for the diagnosis and management of several inflammatory and infectious skin disorders. This article provides an overview of the current available applications of RCM use in cutaneous infections and infestations. PubMed was used to search the following terms in various combinations: reflectance confocal microscopy, skin, hair, nail, infection, parasitosis, mycosis, virus, bacteria. All papers were accordingly reviewed. In most cutaneous infections or infestations, the main alterations are found in the epidermis and upper dermis, where the accuracy of confocal microscopy is nearly similar to that of histopathology. The high resolution of this technique allows the visualization of most skin parasites, fungi, and a few bacteria. Although viruses cannot be identified because of their small size, viral cytopathic effects can be observed on keratinocytes. In addition, RCM can be used to monitor the response to treatment, thereby reducing unnecessary treatments