2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 16 , Issue 2 , PP: 63-85, 2024 | Cite this article as | XML | Html | PDF
Sowmya Koneru 1 , Pappula Madhavi 2 , Krishna Kishore Thota 3 , Janjhyam V. Naga Ramesh 4 , Venkata Nagaraju Thatha 5 , S. Phani Praveen 6 *
Doi: https://doi.org/10.54216/FPA.160205
These days, skin cancer is a prominent cause of death for people. Skin cancer is the name given to the abnormal development of skin cells that are exposed to the sun. These skin cells can develop anywhere on the human body. The majority of malignancies are treatable in the early stages. Thus, early detection of skin cancer is anticipated in order to preserve patient life. With cutting edge innovation, it is possible to detect skin cancer early on. Here, we provide a novel framework for the recognition of dermo duplication pictures that makes use of a neighbouring descriptor encoding method and deep learning technique. Specifically, the deep representations of a rescaled dermo duplication image that were initially removed through training an extraordinarily deep residual neural network on a big dataset of normal images. Subsequently, the neighbourhood deep descriptors are obtained by request-less visual measurement highlights, which rely on fisher vector encoding to create an international image representation. Lastly, a convolution neural network (CNN) was utilised to orchestrate melanoma images employing the Fisher vector encoded depictions. This proposed technique can give more discriminative parts to oversee huge contrasts inside melanoma classes and little varieties among melanoma and non-melanoma classes with least readiness information.
Neural networks , skin cancer , deep learning methodologies , Machine-driven learning , Identification , Assessment.
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