Cloud based mobile solution for early detection of Skin Cancer using Artificial Intelligence
DOI:
https://doi.org/10.32628/CSEIT217327Keywords:
Machine Learning , Artificial Intelligence , Deep Learning , Cloud ComputingAbstract
The vast majority of skin cancer deaths are from melanoma, with about 1.04 million cases annually. Early detection of the same can be immensely helpful in order to try to cure it. But most of the diagnosis procedures are either extremely expensive or not available to a vast majority, as these centers are concentrated in urban regions only. Thus, there is a need for an application that can perform a quick, efficient, and low-cost diagnosis. Our solution proposes to build a server less mobile application on the AWS cloud that takes the images of potential skin tumors and classifies it as either Malignant or Benign. The classification would be carried out using a trained Convolution Neural Network model and Transfer learning (Inception v3). Several experiments will be performed based on Morphology and Color of the tumor to identify ideal parameters.
References
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- F. K. Nezhadian and S. Rashidi, "Melanoma skin cancer detection using color and new texture features," 2017 Artificial Intelligence and Signal Processing Conference (AISP), Shiraz, 2017, pp. 1-5, doi: 10.1109/AISP.2017.8324108.
- Tschandl, Philipp, 2018, "The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions", https://doi.org/10.7910/DVN/DBW86T, Harvard Dataverse
- SIIM-ISIC Melanoma Classification Dataset.
- https://www.frontiersin.org/articles/10.3389/fmed.2020.00233/full
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- https://mc.ai/skin-cancer-image-classification%E2%80%8A-%E2%80%8Aan-educational-guide/?am
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