天池医疗AI大赛[第一季]：肺部结节智能诊断 UNet/VGG/Inception/ResNet/DenseNet

LUNA16-Lung-Nodule-Analysis-2016-Challenge

AiAi.care project is teaching computers to "see" chest X-rays and interpret them how a human Radiologist would. We are using 700,000 Chest X-Rays + Deep Learning to build an FDA 💊 approved, open-source screening tool for Tuberculosis and Lung Cancer. After an MRMC clinical trial, AiAi CAD will be distributed for free to emerging nations, charita…

Diseases Detection from NIH Chest X-ray data

Developing a well-documented repository for the Lung Nodule Detection task on the Luna16 dataset. This work is inspired by the ideas of the first-placed team at DSB2017, "grt123".

This application aims to early detection of lung cancer to give patients the best chance at recovery and survival using CNN Model.

Automatic end-to-end lung tumor segmentation from CT images.

A deep learning-based system for predicting lung cancer from CT scan images using Convolutional Neural Networks (CNN). This project utilizes the Xception model for image classification into four categories: Normal, Adenocarcinoma, Large Cell Carcinoma, and Squamous Cell Carcinoma.

This is a project based on Data Science Bowl 2017. I did my best to propose a solution for the problem but I am still new to Deep Learning so my solution is not the optimal one but it can definitely be improved with some fine tuning and better resources.

Lung nodule detection- LUNA 16

This is a WebApp, which detects lung diseases with integrated stripe payment processing.

Lung Nodules Segmentation from CT scans using CNN.

Gaussian Mixture Convolutional AutoEncoder applied to CT lung scans from the Kaggle Data Science Bowl 2017

A novel pipeline for detecting lung cancer from CT scan images.

ONCO is a cancer diagnosis/prognosis mobile application focused on the 3 main cancers of the thoracic region (Breast, Lung & Skin)

Image classification on lung and colon cancer histopathological images through Capsule Networks or CapsNets.

[ 2017 Graduation Project ] - Pulmonary Nodule Detection & Classification implemented Tensorflow and Caffe1

Boost lung Cancer Detection using Generative model and Semi-Supervised Learning

This project is an end-to-end deep learning pipeline for lung cancer detection using 3D CT scan data.

Program designed to look at X-ray images of Lungs, to analyse and identify tumors. Developed in Matlab, uses custom filter and threshold finding