Developing a CNN-based model to diagnose skin cancer using the ISIC-2019 dataset.
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Jul 6, 2024 - Python
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Developing a CNN-based model to diagnose skin cancer using the ISIC-2019 dataset.
Parses the "Classification of Economic Activities" (wz2008) issued by the Statistisches Bundesamt to build multiple hierarchically structured trees.
This project aims to predict the presence of skin cancer using a hybrid deep learning model that integrates both tabular data and image data. The dataset used is the ISIC dataset, which contains skin lesion images along with associated metadata. The model achieves an accuracy of 88% on the test set.
ISIC Archive API v2 download images by ISIC ID
The aim of this study is to develop a deep learning model using CNNs for accurate skin cancer diagnosis from the ISIC-2019 dataset and to optimize hyperparameters using differential evolution algorithms.
Assignments and Projects of CO410 AI and Expert Systems Course at NITK Surathkal
This project combines traditional machine learning approaches with advanced deep learning techniques to assist healthcare professionals in early diagnosis and improve patient outcomes.
U-Net-based Models for Skin Lesion Segmentation: More Attention and Augmentation
Source code for the paper: "Dermoscopic Dark Corner Artifacts Removal: Friend or Foe?"
A Python SDK Library for working with the International Standard Industrial Classification of All Economic Activities (ISIC), Revision 4.
ISIC2016challenge
The official command line tool for interacting with the ISIC Archive.
This project provides a solution for skin cancer classification using Convolutional Neural Networks (CNN) and Transfer Learning techniques with TensorFlow and Keras. It includes instructions for installation, dataset acquisition, and usage through Jupyter notebooks .
Source code and experiments for the paper: "Dark Corner on Skin Lesion Image Dataset: Does it matter?"
A template for submitting algorithms to the ISIC Challenge
ISIC Challenge submission platform.
Instructions for the removal of duplicate image files from within individual ISIC datasets and across all ISIC datasets.
Add a description, image, and links to the isic topic page so that developers can more easily learn about it.
To associate your repository with the isic topic, visit your repo's landing page and select "manage topics."