TY - GEN
T1 - Computer aided detection system for early cancerous pulmonary nodules by optimizing deep learning features
AU - Elnakib, Ahmed
AU - Amer, Hanan M.
AU - Abou-Chadi, Fatma E.Z.
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery
PY - 2019/4/9
Y1 - 2019/4/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85068608276&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068608276&partnerID=8YFLogxK
U2 - 10.1145/3328833.3328856
DO - 10.1145/3328833.3328856
M3 - Conference contribution
AN - SCOPUS:85068608276
T3 - ACM International Conference Proceeding Series
SP - 75
EP - 79
BT - ICSIE 2019 - Proceedings of 2019 8th International Conference on Software and Information Engineering
PB - Association for Computing Machinery
T2 - 8th International Conference on Software and Information Engineering, ICSIE 2019
Y2 - 9 April 2019 through 12 April 2019
ER -