Research Article Open Access

Detection of Pulmonary Nodules in ct Images Using Deep Learning Technique

Santhi Balachandran1, Divya1, Nithya Rajendran1 and Brindha Giri1
  • 1 SASTRA Deemed to Be University, India
Journal of Computer Science
Volume 16 No. 4, 2020, 568-575

DOI: https://doi.org/10.3844/jcssp.2020.568.575

Submitted On: 15 July 2019 Published On: 15 April 2020

How to Cite: Balachandran, S., Divya, ., Rajendran, N. & Giri, B. (2020). Detection of Pulmonary Nodules in ct Images Using Deep Learning Technique. Journal of Computer Science, 16(4), 568-575. https://doi.org/10.3844/jcssp.2020.568.575

Abstract

Lung Cancer is one of the most deadly diseases worldwide. According to the American Cancer Society, about 234,030 peoples have been suffering from lung cancer. It can be cured if it is diagnosed earlier which decreases the death rate. A computational diagnostic tool named Computer Aided Diagnosis (CAD) is used to detect pulmonary nodules. Extensive work has been made in this domain. However, previous Computer Aided Diagnosis (CAD) system are time-consuming since they needed more modules such as image modification, segmentation and the features should be extracted by the domain experts to build the entire CAD system. It is hard to examine large data using the existing CAD system. Thus, a novel framework with a Convolutional Neural Network (CNN) to detect pulmonary nodule is proposed. Firstly, a preprocessing technique named bilateral filtering is applied to increase the image quality and remove the irrelevant noise from the Computer Tomography (CT) images. Secondly, the preprocessed data are trained into a convolutional neural network to detect the nodule and classify it. The performance of this system is validated using the Lung Image Database Consortium (LIDC) dataset. The accuracy of nodule candidate detection achieves 93%. It states that the proposed method achieves better accuracy in nodule detection.

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Keywords

  • Lung Cancer
  • Computer Aided Diagnosis (CAD)
  • Convolutional Neural Network (CNN)