Build a CNN based model which can accurately detect melanoma

This project uses TensorFlow to implement a Convolutional Neural Network (CNN) for image classification. The goal is to classify skin lesion images into different categories. The dataset used is HAM10000, which contains skin lesion images with associated metadata. The actual accuracy of the model is 90%. 🚀🚀

A Convolutional neural network (CNN)model to train and detect skin cancer (benign and malignant) disease using DDI(Diverse Dermatology Images)Dataset.

A comprehensive guide on how to implement and train a Compact Convolutional Transformer model that is specifically designed for classifying skin cancer.

Skin Cancer Lesion Detection

This repository contains the implementation and supplementary materials for the paper titled "Leveraging Spatial and Semantic Feature Extraction for Skin Cancer Diagnosis with Capsule Networks and Graph Neural Networks."

Deep learning-based skin lesion classification using CNNs for early melanoma detection. Trained on the HAM10000 dataset to improve diagnostic accuracy. 🚀 #AI #DeepLearning #SkinCancerDetection

Classification of skin lesions (among 7 classes) using the file https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T and using the pytorch resnet model. The success rate for the specific test file (unseen data) that comes with the download file is 81.13%.

Binary classification of benign and malignant skin growths using images, feature engineering, and SVMs.