Pulmonary Nodule Classification Software 💝

This software is designed to classify pulmonary nodules into malignant or benign using radiomic features extracted from CT scans. It utilizes machine learning classifiers to efficiently and accurately classify the nodules.

Features

• Extracts radiomic features from CT scans, including shape, texture, and intensity features.
• Utilizes machine learning classifiers, including support vector machines (SVMs) and random forests, for efficient and accurate classification.
• User-friendly interface for easy use, with options to load CT scans, view radiomic features, and display classification results.

Installation

To install the software, follow these steps:

1. Clone the repository: git clone https://github.com/Raallanes/lung-nodule-classification.git
2. Install the required dependencies: pip install -r requirements.txt
3. Run the software: python main.py

Usage

1. Load a CT scan of a pulmonary nodule into the software.
2. The software will automatically extract radiomic features from the scan, including shape, texture, and intensity features.
3. The machine learning classifiers will classify the nodule as malignant or benign based on the extracted features.
4. The results will be displayed on the screen, including the classification result and the probability of malignancy.

References

• Abreu Llanes, R., Pérez Díaz, M., & Galpert, D. (Year). Utilización de características radiómicas en la clasificación maligno/benigno de nódulos pulmonares a partir de TC [PDF document]. Retrieved from [insert URL here]

• Ma, J., Wang, Q., Ren, Y., Hu, H., & Zhao, J. (Year). Automatic lung nodule classification with radiomics approach. In Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations (Vol. 9789, p. 978906). International Society for Optics and Photonics.

• Wang, J., Liu, X., Dong, D., Song, J., Xu, M., Zang, Y., ... & Wong, A. (Year). Prediction of malignant and benign of lung tumor using a quantitative radiomic method. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (Vol. 2016-Octob, pp. 1272-1275