lung-cancer-classification

There are 11 repositories under lung-cancer-classification topic.

  • raun1/ISBI2018-Diagnostic-Classification-Of-Lung-Nodules-Using-3D-Neural-Networks

    Network Architecture for the ISBI_2018 paper : DIAGNOSTIC CLASSIFICATION OF LUNG NODULES USING 3D NEURAL NETWORKS

    Language:Python253321

  • Ala-Eddine-BOUDEMIA/Lung-Cancer-Diagnosis

    Diagnosis of histologic growth patterns of lung cancer in digital slides using deep learning.

    Language:Python4133

  • jlockhar/GLASS-AI

    Machine learning tool for analysis of lung adenocarcinoma tumors

    Language:MATLAB4222

  • karthik-d/lung-tumor-classification

    Deep-learning based classification pipeline for subtyping lung tumors from histology. Study design and codebase to analyze the impact of nucleus segmentation on subtyping.

    Language:Python3202

  • fitushar/Classification-of-chest-CT-using-caselevel-weak-supervision

    Classification of chest CT using caselevel weak supervision

    Language:Python1201

  • TeghSinghJ/lung-cancer-detection-with-svm-algorithm

    Lung Cancer Detection with SVM uses the Support Vector Machine algorithm to detect lung cancer from medical images and patient data. This project covers data preprocessing, feature extraction, model training, and evaluation, aiming to provide a reliable tool for early detection and timely diagnosis.

    Language:Python1100

  • GeorgeBatch/dependency-mil

    [ISBI 2024] Accurate Subtyping of Lung Cancers by Modelling Class Dependencies

    Language:Jupyter Notebook0101

  • intel-comp-saude-ufes/2024-1-P2-classificador-cancer-de-pulmao

    Segundo projeto apresentado na disciplina de Inteligência Computacional em Saúde utilizando a base de dados de imagens histopatológicas de câncer pulmonar.

    Language:Jupyter Notebook0100

  • QuanEvans/Lung_cancer_subtyping

    group project for bioinf575

    Language:Jupyter Notebook0100

  • SpeedKillsx/EasyNodule

    EasyNodule is a software made to help clinicinas to classify Lung Cancer. This will help in elaborating a traitement for the patient which will reduce the progress of the cancer which considered the most killer cancer in the world.

    Language:Python0101

  • xmkrohannon/Cloud-Computing

    The goal of this project was to develop a cloud-based lung cancer classification machine learning model. To this end, 3 different lung cancer datasets were concatenated and combined along common genes. The features from this data set were analyzed using a Random Forest classifier to determine feature importance. The feature importances were evaluated using a standard z-score statistic test, using 2 as the cutoff value, the top ≈2% of features were selected (129 of 5233). Those features were fed into numerous machine learning models, with the best performing model being SVM with a linear kernel, with >90% accuracy.

    Language:Jupyter Notebook0000