7 datasets found

The ISIC Melanoma detection dataset is a public dataset with 2000 training and 600 test images. Each image belongs to one of the three classes: melanoma, seborrheic keratosis,...

The Skin Cancer MNIST (HAM10000) dataset is a good use case to assess the capabilities of attention mechanisms in neural networks.

Automated medical image segmentation can assist doctors to diagnose faster and more accurate. Deep learning based models for medical image segmentation have made great progress...

The ISIC 2019 dataset is a collection of images of skin lesions, and the Pediatric Bone Age dataset is a collection of hand radiographs.

Medical image segmentation is vital to the area of imaging because it enables professionals to more accurately examine and understand the information offered by different...

The dataset used in the paper is HAM10000, which is a dermoscopic image classification dataset. It contains 10,015 dermoscopic images categorized into 7 lesion types.

The dataset contains skin lesion images with annotated lesions.