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Skin Lesion Classification by Using Deep Tree-CNN
Published in Ranjeet Kumar Rout, Saiyed Umer, Sabha Sheikh, Amrit Lal Sangal, Artificial Intelligence Technologies for Computational Biology, 2023
Prakash Choudhary, Sameer Mansuri
Skin cancer constitutes one out of three of the cancers diagnosed worldwide as estimated by the World Health Organization [5]. Melanoma is the most dangerous forms of skin cancer which usually begins developing in skin melanocytes. About Five million people are diagnosed with skin cancer in the US alone every year [18]. The skin cancer is broadly classified into basal cell and carcinomas of skin cancer. The first one (BCC) shows delayed growth to form and thus can be dealt easily. It grows in top skin layer and particularly at the bottom of it. Skin that has not been covered from sunlight for long periods of time shows it's development. It occurs as a thin, flat, glossy, white or waxy lump with hard, dried or scaly patches that can be red or brown in colour [14]. At early stage diagnosis, both of these forms are extremely curable. In the US, 99 out of 100 people survive whose melanoma has been detected at an early stage [14].
Comparison and Performance Evaluation Using Convolution Neural Network-Based Deep Learning Models for Skin Cancer Image Classification
Published in Archana Mire, Vinayak Elangovan, Shailaja Patil, Advances in Deep Learning for Medical Image Analysis, 2022
The term melanoma refers to a serious type of skin cancer. It is produced in the cells responsible for giving skin color – the pigment. Melanin is responsible for pigmentation. Particular cells called melanocytes produce melanin. There are various causes of deformation of melanocyte cells.[3] One reason is skin non-exposure to ultraviolet (UV) light. Lack of skin exposure to UV light raises the probability of melanoma in humans. It is also seen that the risk factor of having melanoma is higher among those under the age of 40, particularly in European countries where sunlight exposure is a problem.[4] Although melanoma is fatal if not treated in the early stages, it is highly curable if detected early.
Intracellular Redox Status and Disease Development: An Overview of the Dynamics of Metabolic Orchestra
Published in Jyoti Ranjan Rout, Rout George Kerry, Abinash Dutta, Biotechnological Advances for Microbiology, Molecular Biology, and Nanotechnology, 2022
Sharmi Mukherjee, Anindita Chakraborty
Conversely, ROS induced melanocyte degeneration generates depigmented macules and patches on the skin known as vitiligo. This is due to the disruption of the Nrf2-p62 signaling pathway that causes autophagy dysregulation and enhances the sensitivity of melanocytes to oxidative stress (He et al., 2017). The tyrosine present in melanocyte gets oxidized into reactive o-quinones generating autoreactive T cells that selectively attack the melanocyte (Singh et al., 2016). Oxidation of sebum increases the level of oxidized lipids like squalene and creates ideal sites for the growth of propionibacterium, which via enhanced ROS production promotes inflammation and add to the severities of acne (Garem et al., 2014). Sebaceous gland oxidations are also associated with seborrheic dermatitis characterized by erythematous patches and scaling on the scalp (Toruan et al., 2017; Trueb et al., 2018). Alopecia areata characterized by nonscarring hair loss are also associated with increased levels of lipid peroxidation products in the scalp, plasma, and erythrocytes (Pektaş et al., 2018; Prie et al., 2015). Increased serum lipid hydroperoxide levels with a shift in thiol/disulfide balance toward disulfides indicate rosacea, an inflammatory dermatosis associated with remissions and flare-ups (Pektas, 2018; Sener et al., 2019).
The effects of sport, setting, and demographics on sunscreen use and education in young athletes
Published in Research in Sports Medicine, 2023
Tracy Zaslow, Akash R. Patel, Rachel Coel, Mia J. Katzel, Tishya A.L. Wren
Sun exposure aids vitamin D absorption and improves bone health; however, overexposure may increase the risk of melanoma and other skin cancers (American Cancer Society, 2019b). Melanoma is a skin cancer caused by uncontrolled growth of melanocytes, which can result in metastatic disease (American Cancer Society, 2019a). In 2023 alone, an estimated 97,610 new melanoma diagnoses will be made in the United States (US), resulting in estimated 7,990 deaths (Siegel et al., 2023). Melanomas account for 3% of cancers identified in adolescents and 1% in children (Siegel et al., 2021) and increased by an average of 2% per year between 1973 and 2009 (Wong et al., 2013). While death from melanomas has been decreasing recently due to advances in treatment (Siegel et al., 2021; Ward et al., 2019), skin cancer from excessive UVR exposure early in life is one of the most preventable types of cancer due to the availability of chemical and physical barriers (De Castro-Maqueda et al., 2021).
Formulation and characteristic evaluation of tacrolimus cubosomal gel for vitiligo
Published in Journal of Dispersion Science and Technology, 2022
Sanjana A, Mohammed Gulzar Ahmed, B.H. Jaswanth Gowda, Suprith Surya
Vitiligo is an acquired idiopathic dermatological disorder characterized by the selective loss of melanocytes, that is, hypomelanosis.[1,2] About 85–95% of the symmetrical lesion’s cases represent non-segmental vitiligo whereas unilateral lesions commonly appear in segmental vitiligo. Younger people more prone to get segmental vitiligo when compared to non-segmental vitiligo.[3] Etiology of vitiligo is unknown, it may be due to lack of immunity power and reduction of melanocyte count in the skin , family history (heredity) or externals factors such as sunburn, stress or exposure to industrial hazards,[4] which results in the smooth, non-scaly, chalky-white macules found in several tissues in the skin, hair follicles, eyes, and inner ear.[5] The prevalence rate of vitiligo is about 0.5–1% of the world population.
Construction of adaptive pulse coupled neural network for abnormality detection in medical images
Published in Applied Artificial Intelligence, 2018
Pawan Kumar Upadhyay, Satish Chandra
The incident of skin cancer continues to increase around the world rapidly. The earlier detection of skin cancer by the advent of advance techniques helps to reduce the further stage of diagnosis such as biopsy (Mishra and Emre Celebi 2016). Skin melanoma is a class of cancer caused by melanocytes (skin pigmentation cells). There are various techniques of image processing, such as pattern analysis, ABCDE, 7-point checklist, Manzies, etc., which are widely used for melanoma detection (Laskaris et al. 2010; Zanottoa and Ballerinib 2011). In addition, the previous literature mainly focuses on classification of melanocytes nevi (moles) from melanoma including some of nonmelanoma class of skin cancer such as carcinoma and keratosis (Ballerini et al. 2013). However, there is adequate amount of literature which emphasizes over the nonmelanoma detection and classification using various machine learning techniques (Ballerini et al. 2012).