# Using VGG16 Algorithms for classification of lung cancer in CT scans Image

arXiv: https://arxiv.org/abs/2305.18367 · HTML: https://mlsanity.com/paper/2305.18367/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 27 May 2023.

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

- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Evaluating LeNet Algorithms in Classification Lung Cancer from Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases](https://mlsanity.com/paper/2305.13333/) (2023)
- [A Novel Bio-Inspired Hybrid Multi-Filter Wrapper Gene Selection Method with Ensemble Classifier for Microarray Data](https://mlsanity.com/paper/2101.00819/) (2021)

## Built on by (most cited first, 2 of 2)

- [Review of deep learning in healthcare](https://mlsanity.com/paper/2310.00727/) (2023)
- [Exploring learning environments for label-efficient cancer diagnosis](https://mlsanity.com/paper/2408.07988/) (2024)

## Abstract (arXiv)

Lung cancer is the leading reason behind cancer-related deaths within the world. Early detection of lung nodules is vital for increasing the survival rate of cancer patients. Traditionally, physicians should manually identify the world suspected of getting carcinoma. When developing these detection systems, the arbitrariness of lung nodules' shape, size, and texture could be a challenge. Many studies showed the applied of computer vision algorithms to accurate diagnosis and classification of lung nodules. A deep learning algorithm called the VGG16 was developed during this paper to help medical professionals diagnose and classify carcinoma nodules. VGG16 can classify medical images of carcinoma in malignant, benign, and healthy patients. This paper showed that nodule detection using this single neural network had 92.08% sensitivity, 91% accuracy, and an AUC of 93%.