Use CNN to detect nodules in LIDC dataset.

Lung cancer detection framework

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Lung Cancer Server For Doctor

Early detection of lung cancer

This project presents the better Computer Aided Diagnosing (CAD) system for automatic detection of lung cancer. The initial process is lung region detection by applying basic image processing techniques such as Bit-Plane Slicing, Erosion, Median Filter, Dilation, Outlining, Lung Border Extraction and Flood-Fill algorithms to the CT scan images. After the lung region is detected, the segmentation is carried out with the help of Mean Shift clustering algorithm. With these, the features are extracted and the diagnosis rules are generated. These rules are then used for learning with the help of Random Forest. The experimentation is performed with 15, 000 images obtained from the kaggle contest. The experimental result shows that the proposed CAD system can able to tell the posterior probability of lung cancer for a patient based on the detection algorithm. Also the usage of Random Forest will increase the accuracy of detecting the cancer nodules.

Lung cancer screening radiomics

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A neural network-based ap proach for optimizing the lung cancer nodule detection and lung cancer stage detection based on pre-processing, segmentation, nodule detection feature extraction and classification of computed tomography scan images.

Lung cancer detection using CNN. The dataset consist of normal and infected lungs images.

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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.

This repository hosts a Michigan Technological University undergraduate senior design team developing a user friendly lung cancer detection software with machine learning capability.

Lung cancer risk prediction using XGBoost

single cell RNA-Seq analysis of human lung cancer. Data from Vivek Mittal <vim2010@med.cornell.edu> and postdoc Sharrell B. Lee <sbl2002@med.cornell.edu>

Ashton, Shoto, Aadity, Akshat, and Ian

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pre-processing and lung cancer detection

https://www.kaggle.com/c/data-science-bowl-2017

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In my LUNA16 dataset cancer detection project, I utilized PyTorch to develop and train convolutional neural networks (CNNs) for accurate identification of lung nodules in 3D CT scans. By leveraging advanced deep learning architectures and extensive data preprocessing techniques, the model achieved high precision and recall.

This project focuses on the prediction of lung cancer using multiple machine learning models and comparing their performances. The dataset used for this project consists of various features related to lung cancer, which were preprocessed, normalized, and then used for training different classifiers.

A machine learning project for classifying lung cancer using CT scan data. It employs semi-supervised learning with Label Spreading to improve classification with limited labeled data, differentiating between malignant and benign lung nodules for early detection.

🔍 Discover the future of healthcare with our Lung Cancer Detection Project. Using advanced machine learning techniques, we've achieved 92% accuracy in identifying lung cancer. Join us at the forefront of medical AI. 👩‍⚕️🌟 #AIHealthcare #LungCancerDetection

Kaggle Data Sicence Bowl 2017

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Predict lungcancer in ct scans, dsb 2017

肺癌中医诊疗智能辅助系统

AI-powered Lung Cancer Disease Classification — A deep learning project developed by Nguyễn Phan Đức Minh for automated lung cancer detection using CT scan images. Built with EfficientNetB1 and transfer learning to achieve high accuracy on the IQ-OTH/NCCD dataset, supporting early diagnosis and medical research.

A AI-RAG service for SCLC patient and families to master knowlege and achieve best treatment benefits