Classification of Lung cancer slide images using deep-learning

Use CNN to detect nodules in LIDC dataset.

Deep Learning for Lung Cancer Risk Prediction using LDCT

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.

Single-cell Lung Cancer Atlas with 1.2M cells

Repository supporting the original research paper in Nature Communications (Primakov et al. 2022)

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

The Project "Exploratory Data Analysis and Prediction of Lung Cancer" is aim towards: finding the insights from the dataset, so that we can create a concrete conclusion out of it and to perform exploratory data analysis. Also we are going to apply machine learning model to predict the lung cancer diagnosis.

This Repository Consist of work related to the detection of Lung Cancer and Malignant Lung Nodules from Chest Radio Graphs using Computer Vision and algorithms, Image Processing and Machine Learning Technology.

A deep learning framework for lung cancer prediction built with Lasagne/Theano

This repository provides scripts to reproduce the results in the paper "An annotation-free whole-slide training approach to pathological classification of lung cancer types by deep learning".

Lung and Colon Cancer Histopathological Image Dataset

:hospital: :eye_speech_bubble: Medical Healthcare AI | Robotic Surgery | Automated Brain Tumour Segmentation | Skin Cancer Lesion Detection & Segmentation (Melonama Recognition) | Lung Cancer detection (Chest CT Scan) | IMAGE SEGMENTATION TECHNIQUES

Lung cancer prediction applies machine learning to classify cases as normal, benign, or malignant using patient data like demographics, symptoms, or imaging. Models like Logistic Regression or CNNs are trained on labeled data and evaluated with metrics like accuracy and AUC-ROC, enabling early detection and timely treatment.

No description available

Scripts for https://www.nature.com/articles/s41598-018-27707-4, using Convolutional Neural Network to detect lung cancer tumor area

Data Science Bowl 2017 : Lung Cancer Detection

This study presents the development and validation of AI models for both nodule detection and cancer classification tasks. This benchmarking across multiple datasets establishes the DLCSD as a reliable resource for lung cancer AI research.

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.

Deep learning-based segmentation and classification of lung nodules

Kaggle Data Science Bowl 2017

Using machine learning and image processing to classify lung cancer in CT scans

Lung cancer risk prediction from CT using fine-tuned I3D neural network

This project uses Deep learning concept in detection of Various Deadly diseases. It can Detect 1) Lung Cancer 2) Covid-19 3)Tuberculosis 4) Pneumonia. It uses CT-Scan and X-ray Images of chest/lung in detecting the disease. It has a Accuracy between 50%-80%. It can take input in any Image format or through Live videos and provide accurate output results.

No description available

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.

3차원 의료영상 데이터의 시각적 설명을 위한 3D LRP SW

This project uses a process known as segmentation to extract individual lung components from CT scans such as the airway, bronchioles, outer lung structure, and cancerous growths. Mathematical descriptions of these objects can be used for AI research, such as predicting benign vs malignant tumors to prevent unnecessary and invasive cancer treatments, early recognition of tumors, and modeling the growth rate of tumors.

Lung Cancer Detection using CNN

personal toolbox for lidc-idri dataset / lung cancer / nodule