Comparison of CNN and YOLOv5 For Melanoma Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Antony:2023:ICCCNT,

• author = "Divya Antony and Naseer C",
• booktitle = "2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)",
• title = "Comparison of {CNN} and {YOLOv5} For Melanoma Detection",
• year = "2023",
• abstract = "MELANOMA is one of the most dangerous forms of skin cancer that results from melanocytes, which produce the brown pigment that gives skin its colour. Sometimes it forms multi-colours based on its stage. The survival of melanoma patients depends on the early identification of the disease. But in the early stage, it becomes very small and does not meet the dermoscopic standards for cancer detection such as irregular shape, network, colour of pigments and also it is difficult to differentiate lesion to benign and melanoma. So early detection of melanoma is difficult. Therefore, we need an accurate melanoma classifier that classifies lesions at begin of the stage. In this paper, a comprehensive analysis of cnn, Alexnet and yolov5 for melanoma detection is performed using Accuracy, Precision, Recall and F1 Score",
• keywords = "genetic algorithms, genetic programming, Multi tree genetic programming, Location awareness, Shape, Melanoma, Colour, Pigments, Skin, Lesions, You only look once, Regional convolutional neural network",
• DOI = "doi:10.1109/ICCCNT56998.2023.10307675",
• ISSN = "2473-7674",
• month = jul,
• notes = "Also known as \cite{10307675}",

}

Genetic Programming entries for Divya Antony Naseer C

Citations