research-article

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

Authors:

Fatma E. Z. Abou-ChadiAuthors Info & Claims

ICSIE '19: Proceedings of the 8th International Conference on Software and Information Engineering

Pages 75 - 79

https://doi.org/10.1145/3328833.3328856

Published: 09 April 2019 Publication History

Abstract

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.

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Cited By

Ruprah TRegmi BJadhav SSingh S(2024)Early Stage Lung Cancer Detection Using Deep Learning2024 MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon)10.1109/MITADTSoCiCon60330.2024.10575345(1-6)Online publication date: 25-Apr-2024
https://doi.org/10.1109/MITADTSoCiCon60330.2024.10575345
C. KVimala HJ. S(2024)A systematic review of artificial intelligence techniques for oral cancer detectionHealthcare Analytics10.1016/j.health.2024.1003045(100304)Online publication date: Jun-2024
https://doi.org/10.1016/j.health.2024.100304
Kudva AKudva VPatil VMohan NShenoy PRai A(2024)Applications of Artificial Intelligence in the Analysis of Images of the Oral Cavity for Cancer DetectionBiomedical Imaging10.1007/978-981-97-5345-1_6(157-169)Online publication date: 27-Sep-2024
https://doi.org/10.1007/978-981-97-5345-1_6

Index Terms

Computer aided detection system for early cancerous pulmonary nodules by optimizing deep learning features
1. Applied computing
  1. Physical sciences and engineering
    1. Engineering
      1. Computer-aided design

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cover image ACM Other conferences

ICSIE '19: Proceedings of the 8th International Conference on Software and Information Engineering

April 2019

276 pages

ISBN:9781450361057

DOI:10.1145/3328833

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 April 2019

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ICSIE '19

ICSIE '19: 2019 8th International Conference on Software and Information Engineering

April 9 - 12, 2019

Cairo, Egypt

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Cited By

Ruprah TRegmi BJadhav SSingh S(2024)Early Stage Lung Cancer Detection Using Deep Learning2024 MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon)10.1109/MITADTSoCiCon60330.2024.10575345(1-6)Online publication date: 25-Apr-2024
https://doi.org/10.1109/MITADTSoCiCon60330.2024.10575345
C. KVimala HJ. S(2024)A systematic review of artificial intelligence techniques for oral cancer detectionHealthcare Analytics10.1016/j.health.2024.1003045(100304)Online publication date: Jun-2024
https://doi.org/10.1016/j.health.2024.100304
Kudva AKudva VPatil VMohan NShenoy PRai A(2024)Applications of Artificial Intelligence in the Analysis of Images of the Oral Cavity for Cancer DetectionBiomedical Imaging10.1007/978-981-97-5345-1_6(157-169)Online publication date: 27-Sep-2024
https://doi.org/10.1007/978-981-97-5345-1_6

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