Use CNN to detect nodules in LIDC dataset.

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.

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.

: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.

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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.

Deep learning-based segmentation and classification of lung nodules

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.

Lung Cancer Detection using CNN

Breast cancer has the second highest mortality rate in women next to lung cancer. As per clinical statistics, 1 in every 8 women is diagnosed with breast cancer in their lifetime. However, periodic clinical check-ups and self-tests help in early detection and thereby significantly increase the chances of survival. Invasive detection techniques cause rupture of the tumor, accelerating the spread of cancer to adjoining areas. Hence, there arises the need for a more robust, fast, accurate, and efficient non-invasive cancer detection system. Early detection can give patients more treatment options. In order to detect signs of cancer, breast tissue from biopsies is stained to enhance the nuclei and cytoplasm for microscopic examination. Then, pathologists evaluate the extent of any abnormal structural variation to determine whether there are tumors. Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer. Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques. AI will become a transformational force in healthcare and soon, computer vision models will be able to get a higher accuracy when researchers have the access to more medical imaging datasets. The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective. We have developed a computer vision model to detect breast cancer in histopathological images. Two classes will be used in this project: Benign and Malignant

REU research detecting lung cancer from CT scans

Lung Nodules Segmentation from CT scans using CNN.

Lung cancer detection framework

Detecting lung cancer from annotated CT scans of lung lesions from the LUNA16 dataset using CNN and GAN

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.

Early detection of lung cancer

Lung cancer detection from images

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Exploratory Analysis + Tutorials for kaggle Data Science Bowl 2017

A novel pipeline for detecting lung cancer from CT scan images.

Early detecting of lung cancer using the Luna data set with LIDC IDRI annotations using two models nodule classification"Googlent model" and the malignancy classification "Lenet model". This was for kaggle's Data science bowl 2017.

Predicting whether a patient has cancer or not

The machine learning project pipeline for lung cancer analysis and prediction at a low cost.

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

Algorithms for improving lung cancer detection with deep learning