octmnist-classifier 0.1.2

A CNN-based classifier for OCTMNIST retinal disease classification with training, prediction, and CLI tools.

OCTMNIST Classifier

A Python package for classifying retinal OCT images into four categories: CNV, DME, Drusen, and Normal, based on the OCTMNIST dataset. Built with a custom CNN and includes preprocessing, class balancing strategies, and a CLI for inference.

🔧 Installation

pip install octmnist-classifier

Make sure to have 'torch', 'medmnist', and other dependencies listed in 'setup.py' installed.

📦 Features

• Preprocessing and loading of the OCTMNIST dataset

• Class balancing using:

  • SMOTE
  • SMOTE + Tomek Links
  • Undersampling

• CNN model with ReLU, BatchNorm, MaxPooling

• Trainable via script interface ('scripts/train.py')

• CLI interface to run inference on new images

🧠 Pretrained Models

Download pretrained models from the Releases page:

• 'model_smote.pt'
• 'model_smote_tomek.pt'

Place them in the 'saved_model/' directory before running predictions.

🚀 Usage

1. Predict a single image

octmnist-predict --image retina.png --model saved_model/model_smote.pt

Output:

Predicted class: NORMAL (Class ID: 3)

2. Training with balancing

python scripts/train.py --strategy smote --epochs 15

Options for '--strategy': 'smote', 'smote_tomek', 'undersample'

🧪 Evaluation (planned)

In future versions, you'll be able to run:

octmnist-eval --model saved_model/model_smote.pt --test_dir test_images/

🔍 Model Architecture

• 4 Conv layers with ReLU, BatchNorm, MaxPooling
• Final Linear layer with Dropout
• Xavier/He initialization supported

🗂️ Project Structure

octmnist-classifier/
├── octmnist_classifier/
│   ├── model.py
│   ├── preprocess.py
│   ├── predict.py
│   └── cli.py
├── saved_model/
│   └── model_smote.pt
├── scripts/
│   └── train.py
└── setup.py

📄 License

MIT License

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Maintained by Kirupanandan Jagadeesan. Contributions and feedback welcome!