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, charitable hospitals, and organizations like WHO 🌏 We will also release our pretrained models and weights as Medical Imagenet.

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".

Diseases Detection from NIH Chest X-ray data

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

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.

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

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.

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

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

The model is based on a YOLOv8 (Deep learning Neural network architecture) and is trained on the publicly available dataset, which consists of lung CT scans of patients with and without lung cancer.

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

This project is a deep learning model for lung cancer prediction, trained on a dataset containing images of different types of lung cancer and normal lung CT scans. The model was created using TensorFlow and Keras, and uses transfer learning with pre-trained models like ResNet50, VGG16, and MobileNetV2.

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

Training a 3D ConvNet to detect lung cancer from patient CT scans, while generating images of lung scans in real time. Adapted from 2017 Data Science Bowl

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

Develop a machine learning (ML) model for lung cancer detection using U-Net and DenseNet architectures. Achieve an accuracy of at least 99.96% in lung nodule detection and classification. Achieved validation of 99.9%.

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

Multiple Disease Prediction System

Here I tried various Machine Learning algorithms on different cancer's dataset present in CSV format.

This repository contains a deep learning-based cancer type prediction system using a trained convolutional neural network (CNN). The model is deployed using Streamlit, allowing users to upload medical images and receive predictions with a probability distribution displayed in a pie chart.

LUng CAncer Screeningwith Multimodal Biomarkers

Understanding Lung CT scans and processing them before applying Machine learning algorithms.

Improve lung cancer detection using deep learning