Streamlit_Malaria-Cell_CNN/README.md

Malaria Cell Classification with CNN and Streamlit Deployment

Welcome to the Malaria Cell Classification project repository! This repository showcases an end-to-end machine learning project for classifying malaria-infected and uninfected cells using Convolutional Neural Networks (CNNs). Additionally, we have implemented a user-friendly web application using Streamlit for easy model inference and deployment.

Project Overview

Malaria remains a significant health challenge in many parts of the world, and rapid and accurate diagnosis is crucial for effective treatment. This project addresses this challenge by leveraging deep learning and computer vision techniques to classify blood cell images as either infected with malaria parasites or uninfected.

Key Features

1. CNN Model

Explore our powerful CNN model trained on a comprehensive dataset of malaria cell images. The model has been fine-tuned to achieve high accuracy in distinguishing infected and uninfected cells.

2. Streamlit Web App

Experience the user-friendly Streamlit web application that allows you to upload cell images and receive real-time predictions from our trained model. It's an intuitive tool for healthcare professionals and researchers.

Contribution

We welcome contributions from the open-source community! If you have improvements, enhancements, or new features to add, please feel free to create pull requests. Together, we can make this project even more valuable.

We hope this project serves as a valuable resource for malaria diagnosis and deep learning enthusiasts alike. Thank you for your interest in our work!