The BCN20000 dataset provides a comprehensive collection of dermoscopic images for training artificial intelligence models in skin cancer diagnosis. It includes a diverse range…
Browsing: Skin Cancer
This article discusses the use of AI and machine learning in dermatology, specifically in the context of skin cancer diagnosis. It examines the potential…
This article discusses the use of deep learning in classifying skin lesions, with the goal of improving accuracy and addressing challenges such as small…
AI has the potential to revolutionize the medical field with its ability to perform tasks that typically require human intelligence. Deep learning algorithms have…
Bdetect, a medical equipment startup, has launched a portable early detection device for melanoma, the most dangerous type of skin cancer. The device uses…
AI is being used to detect cancer in various forms, such as liver, prostate, skin, blood, and stomach cancer. This is possible because AI…
This article discusses an innovative approach for accurately identifying skin cancer using Convolution Neural Network architecture and optimizing hyperparameters. The proposed model utilizes advanced…
This study explored the potential of machine learning to improve the accuracy of skin cancer diagnosis. The decision tree algorithm emerged as the most…
This article reviews the use of image-processing techniques in machine learning (ML) for skin cancer detection using clinical images. It evaluates the efficacy, available…
This article presents a novel skin cancer classification method, SkinFLNet, which utilizes model fusion and lifelong learning technologies. The SkinFLNet was trained using a…