Skin Cancer Detection Using Multi Class CNN Algorithm

Skin conditions, which are already among the most prevalent illnesses in humans, have increased in frequency. Thus, it is crucial to have a diagnosis as soon as possible. Even experienced doctors have difficulty classifying skin ailments and their causes, thus computer-based skin ailment identificat...

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Bibliographic Details
Main Authors: G, Dhivya, K, Luckshana, M, Miduna, K, Nandhini, C, Thilagavathi
Format: Conference Proceeding
Language:English
Subjects:
ABCDE Method
Artificial intelligence
Deep learning
Degrees of severity
Dermatological photograph
Feature extraction
Image color analysis
Image processing
Image segmentation
Medical services
Melanoma
Shape
Skin
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Summary:Skin conditions, which are already among the most prevalent illnesses in humans, have increased in frequency. Thus, it is crucial to have a diagnosis as soon as possible. Even experienced doctors have difficulty classifying skin ailments and their causes, thus computer-based skin ailment identification is required to aid non-specialized people. As a result, technologies for image processing may be utilised to detect skin cancer. Before their various attributes could be recovered, a number of pictures were first segmented. The recommended strategy uses the most basic segmentation technique feasible to accomplish its aims. No human contact is necessary, and the environments for the various kinds of skin lesions do not need to be changed. Here, it is possible to study colour attributes, shape characteristics, and texture characteristics. Convolutional neural networks, a deep learning approach, are used to assess the severity of skin conditions and provide preventive guidance.
ISSN:2575-7288
DOI:10.1109/ICACCS57279.2023.10112987