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- W4387251444 abstract "Skin diseases result from abnormal cell proliferation due to mutations caused by unrepaired DNA damage. They are classified as basal cell, squamous, and MELANOMAs, whereas MELANOMA being a deadly skin cancer that increases mortality. Dermoscopy is used to diagnose MELANOMA in its early phases by identifying its pigmented structures. Effective segmentation of MELANOMA image structures using machine learning models such as the XGBOOST algorithm can improve dermatologists' productivity and accuracy in diagnosing skin diseases. The pigment network's colour, diameter structures, asymmetry, border irregularity, and diameter structures play a crucial role in MELANOMA diagnosis. Identifying a MELANOMA image requires more than two increases dramatically in the near future. Better diagnostics have revealed that the prognosis for a cancer patient is directly related to the disease's stage. It is advised that MELANOMA suspects be detected and treated sooner rather than later to improve the possibility of a successful outcome. Since doctor's subjectivity increases the likelihood of false diagnoses and erroneous results, practical features, and dermoscopy photos help to separate MELANOMA groups. The proposed model can aid in early detection and treatment of MELANOMA, especially in populations with higher risk of developing the disease." @default.
- W4387251444 created "2023-10-03" @default.
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- W4387251444 date "2023-06-23" @default.
- W4387251444 modified "2023-10-06" @default.
- W4387251444 title "Neural Network Architecture for ROI Segmentation Based on Melanoma Detection" @default.
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- W4387251444 doi "https://doi.org/10.1109/icicat57735.2023.10263736" @default.
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