Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images

By Qingzeng Song, Lei Zhao, Xing-cheng Luo, Xuechen Dou. Journal of Healthcare Engineering, 2017

383 citations recorded by the source corpus.

Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depends on the experience of doctors, which may ignore some patients and cause some problems. Deep learning has been proved as a popular and powerful method in many medical imaging diagnosis areas. In this paper, three types of deep neural networks (e.g., CNN, DNN, and SAE) are designed for lung cancer calcification. Those networks are applied to the CT image classification task with some modification for the benign and malignant lung nodules. Those networks were evaluated on the LIDC-IDRI database. The experimental results show that the CNN network archived the best performance with an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32%, which has the best result among the three networks.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ee93681fd9b3b92b3136a3a73289199f87957560

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ahad Nasab
2. Andy Srinivasan
3. Nafiseh Ghaffar Nia
4. A. Asuntha
5. Erkan Kaplanoglu

In Pulmonary and Respiratory Medicine

1. Andy Srinivasan
2. A. Asuntha

In Radiology, Nuclear Medicine and Imaging

1. Andy Srinivasan
2. A. Asuntha

Citations in the corpus

• Deep learning for lung Cancer detection and classification (2020, methodology): “Qing Zeng et al. [29] have applied Deep Learning for Classification of Lung Nodules on CT Images.”
• Evaluation of artificial intelligence techniques in disease diagnosis and prediction (2023, methodology): “Another research compared three deep neural networks model (CNN, DNN, and SAE) to diagnose and classify benign and malignant lung nodules using the LIDC-IDRI database [118].”
• Hybrid deep learning for detecting lung diseases from X-ray images (2020, neutral): “Many researchers have done investigations to relate machine learning schemes for prediction of Xray image diagnostic information [5-7].”