Use CNN to detect nodules in LIDC dataset.

Lung cancer detection framework

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

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

Ashton, Shoto, Aadity, Akshat, and Ian

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

pre-processing and lung cancer detection

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.

🔍 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

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Este repositorio contiene un proyecto en MATLAB para la detección de cáncer de pulmón mediante una interfaz gráfica.

Lung Cancer Detection Using Image Processing

This repository contains the code for the Lung Cancer Detection Using Transformers

Lung Cancer Detection using Deep Learning Dataset: LNDB and LUNA16

2 Machine Learning Models were created. One Convolutional Neural Network and one Support Vector Machine. These models were created to compare the performance of predictive models on detecting various forms of Lung cancer. The Convolutional Neural Network makes use of the VGG 16 model as the base model in place using Transfer learning techniques.

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Lung cancer detection using CT image, OpenCV & Spring Boot

Repository used to store the final project of the deep learning course

FIT3081 Lung Cancer Detection using Matlab

Detect lung nodule in patient

This project is made by me and my teamamtes using python language in anaconda notebook .Lung Cancer Detection using Convolutional Neural Network (CNN) will detect the three types of cancerous images .Hence this project gives above 90% accuracy.

Using a data set of thousands of high-resolution lung scans provided by the National Cancer Institute, participants will develop algorithms that accurately determine when lesions in the lungs are cancerous.

This is a Semester-VI Project based on Machine Learning. This web-app aims to give an accurate prediction of the patient having commonalities associated with Lung Cancer Patients

Cancer is the leading cause of death in the world, with lung cancer having the greatest mortality rates since 1985. Recognizing with higher accuracy and predicting the type of Lung Cancer at the earliest possible stage will help patients have a better chance of surviving

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