Automated Pulmonary Nodule Classification and Detection Using Deep Learning Architectures

Imran Ahmed, Abdellah Chehri, Gwanggil Jeon, Francesco Piccialli

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Recent advancement in biomedical imaging technologies has contributed to tremendous opportunities for the health care sector and the biomedical community. However, collecting, measuring, and analyzing large volumes of health-related data like images is a laborious and time-consuming job for medical experts. Thus, in this regard, artificial intelligence applications (including machine and deep learning systems) help in the early diagnosis of various contagious/ cancerous diseases such as lung cancer. As lung or pulmonary cancer may have no apparent or clear initial symptoms, it is essential to develop and promote a Computer Aided Detection (CAD) system that can support medical experts in classifying and detecting lung nodules at early stages. Therefore, in this article, we analyze the problem of lung cancer diagnosis by classification and detecting pulmonary nodules, i.e., benign and malignant, in CT images. To achieve this objective, an automated deep learning based system is introduced for classifying and detecting lung nodules. In addition, we use novel state-of-the-art detection architectures, including, Faster-RCNN, YOLOv3, and SSD, for detection purposes. All deep learning models are evaluated using a publicly available benchmark LIDC-IDRI data set. The experimental outcomes reveal that the False Positive Rate (FPR) is reduced, and the accuracy is enhanced.

Original languageEnglish
Pages (from-to)2445-2456
Number of pages12
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number4
Publication statusPublished - 1 Jul 2023
Externally publishedYes


  • Biomedical imaging
  • deep learning
  • lung nodule detection
  • transfer learning


Dive into the research topics of 'Automated Pulmonary Nodule Classification and Detection Using Deep Learning Architectures'. Together they form a unique fingerprint.

Cite this