🧪 Medicine Quality Assurance with CNN Model 🌟 Welcome to the Medicine Quality Assurance project! This repository provides a cutting-edge solution for automating the quality testing of pharmaceutical products using deep learning. Leveraging a Convolutional Neural Network (CNN), our model categorizes medicines into three classes—Good, Crack, and Contaminated—ensuring each product meets the highest quality standards. Let’s make healthcare safer, one model at a time! 💊✨

📋 Table of Contents 📘 Project Overview 🎯 Motivation 🔑 Features 🏛 Architecture 🚀 Usage 🤝 Contributing 📜 License 📘 Project Overview Ensuring the quality of medicines is crucial for delivering effective healthcare. Traditional quality control methods rely on human inspection, which can be slow, labor-intensive, and prone to error. This project introduces a CNN-based model for automating this process, classifying medicine images into:

✅ Good: No defects ⚠ Cracked: Physical cracks in tablets 🦠 Contaminated: Presence of impurities or contaminants Our objective is to streamline quality control, enhance accuracy, and ensure that only high-quality medicines make it to the market.

🎯 Motivation Medicines are vital for treating illnesses and maintaining health. However, to be effective and safe, they must undergo strict quality control to meet regulatory standards. Traditional inspection methods can be slow and limited in detecting subtle defects. CNNs provide a solution that can efficiently and accurately identify these flaws, automating what was once a time-consuming process. 💡🔍

🔑 Features ⚡ Automated Defect Detection: Identifies cracks, contaminants, and classifies medicines into quality categories with high precision. 📸 Real-time Image Processing: Analyzes images on the spot, cutting down on manual inspection time. 🧠 Efficient CNN Model: Built with a sequential model structure for quick, reliable predictions. 🕒 Early Degradation Detection: Identifies signs of product instability to assist with stability testing over time. 🏛 Architecture Our model architecture is based on a sequential CNN designed for image classification. Here’s a breakdown:

🎛 Convolutional Layers: Extract spatial features from input images. 📉 Pooling Layers: Downsample feature maps to reduce dimensions. 🔗 Fully Connected Layers: Classify inputs into one of the three classes (Good, Crack, Contaminated). 🧮 Softmax Output Layer: Generates probabilistic output for each class. 🚀 Usage Training the Model:

Organize your dataset into data/ with folders for each class (e.g., Good, Crack, Contaminated). Start training: python Copy code python train_model.py Running Predictions:

Place your test images in the test/ folder. Run predictions: python Copy code python predict.py --image test/sample.jpg Evaluate Model:

Review accuracy and performance metrics: python Copy code python evaluate.py 🤝 Contributing We love contributions! If you’d like to help make this project even better, here’s how to get started:

🍴 Fork the repository. 🌿 Create a new branch (git checkout -b feature-branch). 💬 Commit your changes (git commit -m 'Add new feature'). 📤 Push your branch (git push origin feature-branch). 🔄 Submit a Pull Request. 📜 License Distributed under the MIT License. See LICENSE for more information.

Let’s revolutionize medicine quality testing with AI! 🌍🩺