tanvisenjaliya/SkinCancer
The small size and lack of diversity of the available dataset of dermatoscopic pictures make it difficult to train neural networks for automated identification of pigmented skin lesions. The HAM10000 ("Human Against Machine with 10000 training images") dataset addresses this issue. It consist of dermatoscopic images from various populations, which were captured and preserved using various modalities. There are 10015 dermatoscopic images in the final dataset, which can be used as a training set for academic machine learning. The cases include a diverse range of pigmented lesions, including actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc). The dataset includes lesions with multiple images, which can be tracked by the lesion_id-column within the HAM10000_metadata file. The International Skin Imaging Collaboration (ISIC), a multinational partnership that has created the world's biggest public archive of dermoscopic pictures of skin, held the world's largest skin image analysis challenge. In 2018, the challenge was held in Granada, Spain, at the Medical Image Computing and Computer Assisted Intervention conference. Over 12,500 photos were included in the dataset, which was divided into three jobs. 900 individuals signed up for data download, with 115 completing the lesion segmentation job, 25 the lesion attribute detection task, and 159 the illness classification task