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

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.

Lung Cancer Detection using CNN

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

this website is provide lung cancer detection using cnn model

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This repository deals with the detection of lung cancer nodules in the CT scan images using deep convolutional neural network. The corresponding outputs, dataset and the documents and the google colab codes are available within the repository.

This is Lung Cancer Detection using Deep Learning

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Convolutional Neural networks can identify and classify lung cancer(histopathological dataset) types with greater accuracy in a shorter period, which is crucial for determining patients' right treatment procedure and their survival rate. Benign tissue, Adenocarcinoma, and squamous cell carcinoma are considered in this project

An integrated CNN-SVM model to detect lung cancer on image data set of infected and non-infected data set where tensorflow and keras was used and by importing svm i used Squared_hinge as loss function to import the SVM and got accuracy of 97.4%

This project is a deep learning-based application for detecting lung cancer from CT scan images using a Convolutional Neural Network (CNN). It includes a full GUI built with Tkinter, allowing users to upload CT scan images and receive predictions in real time.

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This project is done during the NIELIT Training on Deep Learning Using Python

This is the lung cancer detection using cnn website by akshay kalwaghe.

ICSI435/535 AI Project

Study project on lung cancer detection using Convolutional Neural Networks (CNNs) applied to CT scan images. Not intended for medical use.

Lung Cancer Image Classification is a deep learning project that uses convolutional neural networks (CNNs) to analyze CT scans for early detection of lung cancer. Implemented in a Jupyter notebook, it features data preprocessing, model training, and visualization using the Lung and Colon Cancer Histopathological Image Dataset (LC25000).

A Deep Learning-based Lung Cancer Detection application using a 3D CNN model with TensorFlow and OpenCV, featuring an interactive Tkinter GUI for easy data processing and training.

A CNN-based approach for early lung cancer detection using CT scans. Achieves 97.51% accuracy with optimized hyperparameters. Built with Python, TensorFlow, OpenCV, and Streamlit for real-time analysis. Contribute or explore the code to advance AI-driven medical diagnostics!

Implementing a lung cancer detection system using Convolutional Neural Networks (CNNs) for early diagnosis and improved patient outcomes.

This project focuses on developing a deep learning model for lung cancer detection using Convolutional Neural Networks (CNNs). The model is trained on a lung cancer image dataset and optimized for deployment as a web application.

Lung Cancer Detection using CNN

Lung Cancer detection using CNN

The project "Lung Cancer Detection Using CNN" focuses on developing a deep learning-based approach for early and accurate detection of lung cancer using medical imaging data such as CT scans or X-rays. Early detection is critical for improving survival rates and treatment outcomes.

By using Convolutional Neural Networks (CNN) to Identifying Medical Images such as X-rays and CT scans

This is my first git repository

A deep learning project that uses Convolutional Neural Networks (CNNs) to detect lung cancer from CT scan images. The model classifies images as benign, malignant, or normal, enabling early diagnosis and faster medical analysis.

In this project, we have implement a CNN suitable for the analysis of CT scans with CT images, using domain knowledge from both medicine and neural networks. This result shows where patient has cancer or normal.