# Classification and Segmentation of Pulmonary Lesions in CT Images Using a Combined VGG-XGBoost Method, and an Integrated Fuzzy Clustering-Level Set Technique

arXiv: https://arxiv.org/abs/2101.00948 · HTML: https://mlsanity.com/paper/2101.00948/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)

## Abstract (arXiv)

Given that lung cancer is one of the deadliest illnesses, early identification and diagnosis are critical to preserving a patient's life. However, lung illness diagnosis is time-intensive and requires the expertise of a pulmonary disease specialist, subject to a significant rate of inaccuracy. Our objective is to design a system capable of accurately detecting and classifying lung lesions and segmenting them in CT-scan images. The suggested technique extracts features automatically from the CT-scan image and then classifies them using Ensemble Gradient Boosting methods. Finally, if a lesion is detected in the CT-scan image, it is segmented using a hybrid approach based on Fuzzy Clustering and Level Set. To train and test our models we gathered a dataset that included CT images of patients residing in Mashhad, Iran. Finally, the results indicate 96% accuracy within this dataset. This approach may assist clinicians in diagnosing lung abnormalities and avoiding potential errors.