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Erschienen in:
2024 | OriginalPaper | Buchkapitel
Melanoma Detection Using Convolutional Neural Networks
verfasst von : Venkata Sai Geethika Avanigadda, Ravi Kishan Surapaneni, Devika Moturi
Erschienen in: High Performance Computing, Smart Devices and Networks
Verlag: Springer Nature Singapore
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Abstract
The prevalence of skin cancer is a huge social issue. Melanoma is one type of skin cancer which is known as malignant melanoma. It is the most dangerous skin cancer which is spreading more vastly. Melanoma makes up the majority of skin cancer deaths roughly 75% of them. Detecting melanoma cancer as early as possible and receiving therapy with little surgery are the best ways to beat it. This model quickly categorizes melanoma disease by utilizing efficient higher resolution convolutional neural networks. By using the efficient MobileNetV2 architecture model, the automated melanoma detection model can be developed to identify the skin lesion images. The MobileNetV2 architecture is incredibly lightweight and can be utilized to extract more functionality. The HAM10000 dataset has been used for the evaluation. It uses the global average pooling layer which is connected with the fully connected layers. The proposed system can be used to detect whether the disease is melanoma or not. The model has an accuracy rate of 85%.