| Abstrakt: |
Skin cancer is caused by genetic uncertainty or an irregular growth of cells, mostly grows when our skin is exposed to sun. In people, melanoma is a common type of cancer irrespective of their age, gender or race. Skin cancer has mainly two types: malignant melanoma and non-melanoma. Now days, the leading cancer type in people is melanoma among many white-skinned populations, while non-melanoma skin cancer is common in light-skinned population. The melanoma can transfer to different body parts if it is left untreated. The computerized system is needed for melanoma cancer risk evaluation, as the dermatologist monitors the condition of the patient by using skin cancer images and dermatoscope. Its identification in advance, needs the proper identification of spots on skin which is based on some features, which are extracted. Different segmentation methods play very important role in skin lesion detection from images, in which segmentation performs the image classification, into the pixels of skin and non- skin based upon the texture of skin. The convolutional neural networks (CNNs) have presented better outcomes in the skin cancer classification as compared to the findings of dermatologists. This model will help people to get help from their installed applications on mobile phone or electronic devices, for the early diagnosis of cancer. In this review study, various research papers have been presented on the skin lesions classification based on CNNs. We have discussed how CNNs have a great impact in successful skin cancer classification and methods which are implemented with success rates. Deep learning using CNN also has many advantages and, there is some vulnerability as well in misclassifying the images, under some situations. In this paper we searched IEEE, Science Direct, Google Scholar, Elsevier, PubMed and Web of Science databases to obtain original published research articles and selected papers providing necessary information related to the research and finally we have produced a systematic review on published methodologies, datasets, algorithms, results, accuracy, sensitivity etc. using CNN model. [ABSTRACT FROM AUTHOR] |
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