ham10000

There are 23 repositories under ham10000 topic.

  • xmindflow/DermoSegDiff

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

    Language:Python615910

  • Woodman718/FixCaps

    FixCaps: An Improved Capsules Network for Diagnosis of Skin Cancer，DOI: 10.1109/ACCESS.2022.3181225

    Language:Jupyter Notebook31178

  • batmanlab/ICML-2023-Route-interpret-repeat

    Official repository of ICML 2023 paper: Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat

    Language:Jupyter Notebook22314

  • niyazed/Dermatology-Image-Classification

    The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.

    Language:Jupyter Notebook14108

  • deveshsangwan/Skin-lesions-classification

    This repo includes classifier trained to distinct 7 type of skin lesions

    Language:Jupyter Notebook9107

  • Faysal-MD/An-Interpretable-Deep-Learning-Approach-for-Skin-Cancer-Categorization-IEEE2023

    Multiclass skin cancer detection using explainable AI for checking the models' robustness

    Language:Jupyter Notebook7214

  • msthoma/LesionDetector

    Cross-platform smartphone app capable of detecting skin cancer lesions using Computer Vision.

    Language:Dart4102
  • SkinLesionAI

    h-ssiqueira/SkinLesionAI

    Notebooks of pre trained models using the HAM10000 dataset

    Language:Jupyter Notebook3101

  • kakumarabhishek/Corrected-Skin-Image-Datasets

    Data and code for our analysis of DermaMNIST (MedMNIST), HAM10000, and Fitzpatrick17k datasets

    Language:Jupyter Notebook3100

  • msthoma/HAM10000_ConvNet

    Convolutional neural network capable of identifying skin lesions (based on the skin lesion image data set HAM10000).

    Language:Jupyter Notebook3202

  • shobhitsinha-A/Artificial-Intelligence-Project

    This is a project that I worked on with my colleagues in the 6th Semester of my B.tech. In this project, we present a fully automatic method for skin lesion segmentation by leveraging UNet and FCN that is trained end to-end. For Skin lesion disease classification, we use a customized convolutional neural net. Designing a novel loss function based on Jaccard distance to eliminate the need of sample re-weighting, a typical procedure when using cross entropy as the loss function for image segmentation due to the strong imbalance between the number of foreground and background pixels.  Evaluated the effectiveness, efficiency, as well as generalization capability of the proposed framework on publicly available PH2 database and HAM10000 dataset.

    Language:Jupyter Notebook3110

  • Otatoess/HAM-skincancer

    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.

    Language:Python2201

  • SCASTELLANO6044/TFT-AI

    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%. 🚀🚀

    Language:Python2300

  • SpyrosAlvanakis/DNN_Ham10000

    HAM10000 image dataset classification using Pytorch and Scikit Learn

    Language:Jupyter Notebook2202

  • amir-not2005/dermamarker

    Website for Skin cancer detection based on HAM10000 | Dropbox Integration

    Language:CSS1100

  • PROxZIMA/Skin-Cancer-MNIST-HAM10000

    Skin Cancer MNIST: HAM10000 - ResNet50 vs Inception-V3 vs VGG-19 vs VGG-16 vs GoogLeNet (Inception-V1)

    Language:Jupyter Notebook1201

  • achraf-oujjir/xception-on-ham10k

    In this project, we used a transfer learning approach to build an image classification model for the classification of skin lesion, we trained our model specifically on the ham10000 dataset available on kaggle and we were able to achieve a 93.6% accuracy

    Language:Jupyter Notebook00

  • ADITYAVOFFICIAL/Skin-Disease-Classifier

    Discover DermaScan: A full-stack web app with MobileNetV2-based skin lesion classifier using Harvard's Ham10000 Dataset for precise dermatological diagnosis.

    Language:Jupyter Notebook0100

  • anwai98/Skin-Lesion

    Diagnosis of Dermoscopic Images using Multi-Sizing Ensemble-Based Deep Learning Method

    Language:Jupyter Notebook0102

  • MeghanaMsl/Skin-Lesion-Segmentation-Classification

    Terminal application to perform skin lesion segmentation & classification

    Language:Jupyter Notebook0101

  • Mousteph/Skin_disease

    This project is designed for classifying various skin diseases using the HAM10000 dataset. It leverages a trained model, explains predictions using LIME, and provides multiple interfaces for users, including a server, a graphical user interface, a command-line interface, and an API.

    Language:Jupyter Notebook0100

  • saeid436/DASC5420-FinalProject

    This is for Final Project of Theoretical Machine Learning Course...

    Language:Jupyter Notebook0100

  • arindam369/FuzzyEnsemble-SkinCancer

    This repository accompanies our research paper and includes all the essential files that support our findings on fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification

    Language:Jupyter Notebook