This project focuses on developing a deep learning model to detect pneumonia from chest X-ray images. The aim is to assist healthcare professionals in diagnosing pneumonia quickly and accurately using image classification techniques.

This project provides automated classification between Normal and Pneumonia cases with confidence scores and visual heatmaps.

Deep learning CNN model for pneumonia classification in chest X-rays. Optimized 3-layer architecture achieves 93.33% recall and 84.78% accuracy. Resource-efficient training (80% CPU), 75MB model, sub-2s inference. Live demo deployed on Streamlit with professional medical interface.

This project uses deep learning algorithms and the Keras library to determine if a person has certain diseases or not from their chest x-rays and other scans. The trained model is displayed using Streamlit, which enables the user to upload an image and receive instant feedback.

A baseline pneumonia classification system using DenseNet-121 on chest X-ray images. This implementation provides patient-level data splits, multiple preprocessing pipelines, class balancing, and interpretability analysis with Grad-CAM visualizations.

Two-stage chest X-ray classifier: NORMAL vs PNEUMONIA → BACTERIA vs VIRUS. Grad-CAM + Streamlit demo. Educational—not for clinical use.

A medical imaging project using the RSNA Pneumonia Detection Challenge dataset (Chest X-rays)

This project is all about interpreting Chest X-ray images, and the task is to classify whether X-ray image got infected by Pneumonia or not. we also used here GAN and various type of augmentation techniques.

This project is an AI-powered medical diagnosis assistant that identifies pneumonia from chest X-ray images using deep learning. Built with PyTorch and trained on a Kaggle dataset, the model leverages the ResNet18 architecture to accurately classify whether a patient has pneumonia or not.

Machine learning project for classifying chest X-rays as healthy or pneumonia-affected, featuring multiple algorithms and imbalance handling techniques.

This project utilizes (CNN) to accurately classify X-Ray images for pneumonia detection. It explores three different approaches to handle data imbalance and achieve optimal model performance. The project includes detailed evaluation metrics and use Streamlit which enables a seamless classification.

Pneumonia classificator using Keras with Streamlit

This project implements an AI-powered Pneumonia Detection and Analysis System using a Convolutional Neural Network (CNN). The system is designed to detect pneumonia from chest X-ray images, providing predictions and detailed analysis through an interactive user interface built with Streamlit.

This repository includes necessary files, codes required to run Pneumpredict and predict the health of lungs from X-rays.

Detección de Neumonia con IA - online

CS404 Artificial Intelligence final project. This project is based on the Pneumonia Images dataset found on Kaggle. The goal was to classify the images using classic Artificial Neural Networks.

Classify Chest X-ray image to pneumonia or normal.

A neural network that analyses an x-ray of a person's lungs and can identify with 75-85% accuracy whether they have COVID-19, pneumonia, or are healthy (or are asymptomatic)

Pneumonia Detection using Deep Learning