🦷🪥 Dental Image Denoising and Classification with CNN, MLP, VGG, ResNet and Autoencoder

This repository contains implementations of various deep learning architectures for classifying images from the popular MNIST dataset 🖼️. The MNIST dataset consists of 70,000 grayscale images of handwritten digits (0-9), each of size 28x28 pixels. This project explores the performance of different models for image classification, including:

• 🌐 CNN (Convolutional Neural Network)
• 🖥️ MLP (Multilayer Perceptron)
• 🔍 VGG-like CNN
• 🌀 ResNet-like CNN
• 🔄 Autoencoder for Feature Learning

🚀 Project Overview

The goal is to classify handwritten digits using multiple deep learning models and evaluate them based on common metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. The project includes:

1. Data Preprocessing: Reshaping, normalizing, and splitting the dataset.
2. Model Architectures:
   • CNN: A simple convolutional neural network.
   • MLP: A fully connected network for baseline performance.
   • VGG-like CNN: A deeper CNN model inspired by VGGNet.
   • ResNet-like CNN: A residual network with skip connections.
   • Autoencoder: For unsupervised feature learning.
3. Training & Evaluation: Models are trained for 10 epochs and evaluated on the test set.
4. Visualization:
   • Confusion Matrix 📊
   • ROC Curve 📈

📋 Evaluation Metrics

The models are evaluated using the following metrics:

• Accuracy: Overall correctness of the model's predictions.
• Precision: Proportion of correctly predicted positive observations to the total predicted positives.
• Recall: Proportion of correctly predicted positive observations to all observations in the actual class.
• F1 Score: Harmonic mean of precision and recall.
• ROC-AUC Score: The Area Under the ROC Curve to evaluate model performance across all classification thresholds.

⚙️ How to Run

1. Clone the repository:

   git clone https://github.com/yourusername/mnist-classification.git

2. Install the required libraries:

   pip install -r requirements.txt

3. Run the training and evaluation script:

   python train_and_evaluate.py

📚 Libraries Used

• numpy for numerical operations
• matplotlib & seaborn for visualizations
• tensorflow/keras for deep learning models
• scikit-learn for evaluation metrics

📝 Conclusion

This project provides insights into how different neural network architectures perform on the MNIST dataset. Explore the results to see which model works best for your image classification tasks! 😊

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