Computer aided detection system for early cancerous pulmonary nodules by optimizing deep learning features

Ahmed Elnakib, Hanan M. Amer, Fatma E.Z. Abou-Chadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


In this paper, a deep learning technique for the early detection of pulmonary nodules from low dose CT (LDCT) images is proposed. The proposed technique is composed from four stages. Firstly, a preprocessing stage is applied to enhance image contrast of low dose images. Secondly, a transfer learning is utilized to extract deep learning features that describe the LDCT images. Thirdly, a genetic algorithm (GA) is learned on the extracted deep learning features using a training subset of the data to optimize the feature-set and select the most relevant features for cancerous nodules detection. Finally, a classification step of the selected features is performed using supported vector machines (SVM) to detect cancerous pulmonary nodules. Preliminary results on a number of 320 LDCT images acquired from 50 different subjects from the International Early Lung Cancer Action Project, I-ELCAP, online public lung image database has achieved a detection accuracy of 92.5%, sensitivity of 90%, and specificity of 95% Comparison results has shown the outstanding results of the proposed method. These preliminary results confirm the promising of our proposed method.

Original languageEnglish (US)
Title of host publicationICSIE 2019 - Proceedings of 2019 8th International Conference on Software and Information Engineering
PublisherAssociation for Computing Machinery
Number of pages5
ISBN (Electronic)9781450361057
StatePublished - Apr 9 2019
Event8th International Conference on Software and Information Engineering, ICSIE 2019 - Cairo, Egypt
Duration: Apr 9 2019Apr 12 2019

Publication series

NameACM International Conference Proceeding Series


Conference8th International Conference on Software and Information Engineering, ICSIE 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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