Abstract: Dermatology remains one of the foremost branches of science that is uncertain and complicated because of the sheer number of diseases that affect the skin and the uncertainty surrounding their diagnosis. The variation in these diseases can be seen because of many environmental, geographical, and gene factors and the human skin is considered one of the most uncertain and troublesome terrains particularly due to the presence of hair, its deviations in tone and other similar mitigating factors. Skin disease diagnosis at present includes a series of pathological laboratory tests for the identification of the correct disease and among them, cancers of the skin are some of the worst. Skin cancers can prove to be fatal, particularly if not treated at the initial stage. The idea behind this project is to make it possible for a common man to get a sense of the disease affecting his/her skin so they can get a head start in preparing for its betterment and the doctor in charge can get an idea about the type of cancer which helps them in the diagnosis. Users are greeted with a login page, and when they log into the home page, users can upload an image of the diseased part of their skin. The trained model gives a prediction, following which the users can take the necessary steps to contain the disease.
Cite:
Aishwarya Tamse, Annapoorna Pai, Arundhathi Nayak, Mithali Prashanth Rao, Shreejith K B*, "A DEEP LEARNING APPROACH TO DETECT SKIN CANCER USING DERMOSCOPIC IMAGES", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13367.
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DOI:
10.17148/IJARCCE.2024.13367
