Skin Cancer Detection Using Machine Learning
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Abstract
Skin-related issues often serve as indicators of underlying health problems in other parts of the human body, adversely affecting an individual's overall fitness and well-being. However, such issues are frequently ignored, as they are perceived to be either painless or have minimal to no impact on daily life. To address this challenge, this system was developed with the aim of early detection of skin cancer. The system enables users to upload images or videos of the affected area in real-time, utilizing a skin cancer detector to identify existing conditions, determine the cancer type (if present), and provide instant feedback. The system is precisely configured to deliver highperformance results, offering real-time recommendations for medications or treatments based on its findings. By reducing the stress and inconvenience associated with hospital visits for dermatological consultations, this system empowers individuals to identify skin-related issues accurately and make informed decisions about seeking further medical attention from a qualified dermatologist. Powered by image processing using OpenCV, a convolutional neural network (CNN), and machine learning techniques, the system excels in recognizing various skin conditions with high accuracy. It can detect conditions such as nevus, vascular lesions, seborrheic keratosis, basal cell carcinoma, melanoma, pigmented benign keratosis, squamous cell carcinoma, dermatofibroma, and actinic keratosis, while also recommending appropriate remedial health solutions.
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