Abstract
Diagnosis of skin cancer at an early stage poses a great challenge even in the twenty-first century due to complex and expensive diagnostic techniques currently used for detection. Furthermore, traditional detection techniques are highly dependent on human interpretation. In case of fatal diseases such as melanoma, detection in early stages plays a vital role in determining the probability of getting cured. Several techniques such as dermoscopy, thermography and sonography are used for skin cancer detection, but every technique has its own limitations. Also, it is not feasible for every suspected patient to receive intensive screening by dermatologists. These limitations suggest the need for development of a simpler, cheaper, minimal invasive and accurate methodology independent of human intervention for skin cancer detection. Advancements in various computer vision algorithms have led to their extensive use in the area of bioinformatics. Therefore, this research paper aims to resolve the problem of early detection of skin cancer with a higher accuracy than existing methodologies using computer vision.
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Khan, Z., Shubham, T., Arya, R.K. (2022). Skin Cancer Detection Using Computer Vision. In: Mandal, J.K., Hsiung, PA., Sankar Dhar, R. (eds) Topical Drifts in Intelligent Computing. ICCTA 2021. Lecture Notes in Networks and Systems, vol 426. Springer, Singapore. https://doi.org/10.1007/978-981-19-0745-6_1
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DOI: https://doi.org/10.1007/978-981-19-0745-6_1
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