[MICCAI 2023] DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation

[MICCAI 2025] CENet: Context Enhancement Network for Medical Image Segmentation

PyTorch code to reproduce the key experiments and results presented in the paper: ELMAGIC: Energy-Efficient Lean Model for Reliable Medical Image Generation and Classification Using Forward Forward Algorithm.

This repository contains a deep learning model for skin cancer classification using the InceptionV3 architecture. The model was trained on the HAM10000 dataset and is designed with computational efficiency in mind. It was developed to be able to run on a CPU.

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%. 🚀🚀

🏥 Detect cancer using an AI model trained on multiple datasets, designed for research and educational purposes only. Not for clinical use.

An ML pipeline to classify skin lesions as malignat or benign with a data dashboard and a API interface tested on the HAM10000

Deep learning models for skin lesion classification on HAM10000 dataset using CNNs and transfer learning (MobileNet, ResNet, DenseNet).

Deep learning project for skin lesion classification using CNNs and calibration methods. Includes training, evaluation, and visualization scripts.

Este repositório contém dois treinamentos de Redes Neurais Convolucionais (CNNs) desenvolvidas em PyTorch.