Melanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized Dermoscopic Skin Lesion Classification
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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Skin cancer has been the most usual and illustrates 50% of all new cancers detected each year. If they detected at an early stage, treatment can become simple and economically. Accurate skin lesion segmentation is important in automated early skin cancer detection and diagnosis systems. The aim of this study is to provide an effective approach to detect the skin lesion border on a purposed imag...
متن کاملA Novel Method for Skin Lesion Segmentation
Skin cancer has been the most usual and illustrates 50% of all new cancers detected each year. If they detected at an early stage, treatment can become simple and economically. Accurate skin lesion segmentation is important in automated early skin cancer detection and diagnosis systems. The aim of this study is to provide an effective approach to detect the skin lesion border on a purposed imag...
متن کاملSkin Lesion Analysis towards Melanoma Detection Using Deep Learning Network
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...
متن کاملUsing 3D information for classification of non-melanoma skin lesions
New sensors allow simultaneous acquisition of 3D shape and colour data of skin at resolutions theoretically approaching cellular structures. We investigate whether the addition of 3D depth information increases classification rates relative to only using colour information for 5 non-melanoma skin lesions. The paper demonstrates that there is 6% increase in classification rates.
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ژورنال
عنوان ژورنال: IEEE Journal of Translational Engineering in Health and Medicine
سال: 2017
ISSN: 2168-2372
DOI: 10.1109/jtehm.2017.2648797