The dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes. The objective to build deep learning model to classify given query image into one of the 7 different classes of skin cancer.

NTI Project – Segmentation, Object Detection, and Classification on Skin Cancer MNIST: HAM10000 dataset

Skin Cancer Detection using Deep learning (CNN). The DatasetLink For skin cancer detection : https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000

This project employs AI for early skin cancer detection, using deep learning and image analysis. It utilizes the Skin Cancer 9 Classes ISIC and Skin Cancer MNIST HAM10000 datasets from Kaggle. With diverse data, it accurately classifies lesions, aiding in timely diagnoses for better patient outcomes.

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skin-cancer-detection-using-MNIST: HAM10000-dataset

No description available

Developed a skin cancer detection model using the VGG16 architecture and the MNIST dataset of skin cancer images. The project involved data preparation, augmentation, model building, and training. Achieved a validation accuracy of ~78%, demonstrating the model's potential in early skin cancer detection.

This project aims to automatically classify skin lesions from dermatoscopic images using a Convolutional Neural Network (CNN) trained on the Skin Cancer MNIST: HAM10000 dataset. The system assists in early detection of potential skin cancers such as melanoma and basal cell carcinoma, supporting dermatologists and public awareness of skin health.

Deep learning project for melanoma detection using the Skin Cancer MNIST: HAM10000 dataset. Combines image preprocessing, CNNs, and transfer learning to classify skin lesions. Public for learning, but direct contributions are limited to approved collaborators.

Deep learning project for melanoma detection using the Skin Cancer MNIST: HAM10000 dataset. Combines image preprocessing, CNNs, and transfer learning to classify skin lesions. Public for learning, but direct contributions are limited to approved collaborators.