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