Multimodal skin lesion classification using deep learning
نویسندگان
چکیده
منابع مشابه
Deep Learning for Skin Lesion Classification
Melanoma, a malignant form of skin cancer is very threatening to life. Diagnosis of melanoma at an earlier stage is highly needed as it has a very high cure rate. Benign and malignant forms of skin cancer can be detected by analyzing the lesions present on the surface of the skin using dermoscopic images. In this work, an automated skin lesion detection system has been developed which learns th...
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Melanoma is a malignant tumour originating from melanocytes cells skin cells responsible for the production of melanin. The American Cancer Society estimates that in the United States alone for 2017, more than 87,000 new melanoma cases will be diagnosed and around 9,300 persons are expected to die[1]. Skin melanoma lesions are very challenging to visually diagnose due to their similarity in vis...
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Skin cancer is one of the major types of cancers and its incidence has been increasing over the past decades. Skin lesions can arise from various dermatologic disorders and can be classified to various types according to their texture, structure, color and other morphological features. The accuracy of diagnosis of skin lesions, specifically the discrimination of benign and malignant lesions, is...
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Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is v...
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Within medical imaging, manual curation of sufficient welllabeled samples is cost, time and scale-prohibitive. To improve the representativeness of the training dataset, for the first time, we present an approach to utilize large amounts of freely available web data through web-crawling. To handle noise and weak nature of web annotations, we propose a two-step transfer learning based training p...
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ژورنال
عنوان ژورنال: Experimental Dermatology
سال: 2018
ISSN: 0906-6705
DOI: 10.1111/exd.13777