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Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review.

BMC Cancer

January 2025

Department of Data Science, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran.

Background: Melanoma is a highly aggressive skin cancer, where early and accurate diagnosis is crucial to improve patient outcomes. Dermoscopy, a non-invasive imaging technique, aids in melanoma detection but can be limited by subjective interpretation. Recently, machine learning and deep learning techniques have shown promise in enhancing diagnostic precision by automating the analysis of dermoscopy images.

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Our study aimed to investigate the correlation between skin cancer and anti-interleukin (IL) therapy in patients with moderate-to-severe psoriasis. This was an observational monocentric study in which we enrolled a total of 235 patients in which 127 patients were affected by moderate-to-severe psoriasis and treated with anti-IL monoclonal antibodies (mAbs) for at least 6 months, whereas 108 patients affected by mild psoriasis were treated with topical therapies. Afterward, we performed a dermatologic visit to all the subjects, collecting anamnestic information including risk factors for skin cancer.

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Skin cancer is one of the most prevalent cancers worldwide, with increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Although melanoma is less common than basal or squamous cell carcinomas, it is the deadliest form of cancer, with nearly 8300 Americans expected to die from it each year.

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Background/objectives: Skin cancer is becoming increasingly common due to increasing risk factors such as excessive ultraviolet (UV) radiation, genetic predisposition, fair skin, and a history of sunburn. Melanoma accounts for only 1% of cases but causes most skin cancer deaths. Dysplastic nevi (DN) are important precursors of melanoma.

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