TY - GEN
T1 - Automatic nucleus detection of pap smear images using stacked sparse autoencoder (SSAE)
AU - Mufidah, Ratna
AU - Faturrahman, Moh
AU - Wasito, Ito
AU - Ghaisani, Fakhirah D.
AU - Hanifah, Nurul
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/8/10
Y1 - 2017/8/10
N2 - Pap smear image analysis is an effective and common way for early diagnosis of cervical cancer. Nucleus and cytoplasm morphology analysis are main criterion in determining whether the cells are normal or abnormal. Therefore, the accuracy of nucleus detection is crucial before further analysis of cell changes. One of the main problem in automatic nucleus detection process on pap smear image is how to accurately detect the nucleus on multi-cell image which usually contain overlapped cells. To solve the problem, authors propose a deep learning (DL) approach in particular Stacked Sparse Autoencoder (SSAE) as a feature representation process in multi-cell pap smear images. SSAE is able to capture high level feature through learning processing from low level feature (pixel). The high level feature will be a differentiator feature between nucleus and non-nucleus. In this research, authors have applied sliding window operation (SWO) on pap smear images and utilized softmax classifier (SMC) for the nucleus classification process. The main purpose in this research is to measure the performance of SSAE+SMC for the detection of nucleus on overlapped cells. The result shows that fine-tuned SSAE+SMC has significantly increased the accuracy of nucleus detection. The best accuracy achieves 0.876 on 50 × 50 window size.
AB - Pap smear image analysis is an effective and common way for early diagnosis of cervical cancer. Nucleus and cytoplasm morphology analysis are main criterion in determining whether the cells are normal or abnormal. Therefore, the accuracy of nucleus detection is crucial before further analysis of cell changes. One of the main problem in automatic nucleus detection process on pap smear image is how to accurately detect the nucleus on multi-cell image which usually contain overlapped cells. To solve the problem, authors propose a deep learning (DL) approach in particular Stacked Sparse Autoencoder (SSAE) as a feature representation process in multi-cell pap smear images. SSAE is able to capture high level feature through learning processing from low level feature (pixel). The high level feature will be a differentiator feature between nucleus and non-nucleus. In this research, authors have applied sliding window operation (SWO) on pap smear images and utilized softmax classifier (SMC) for the nucleus classification process. The main purpose in this research is to measure the performance of SSAE+SMC for the detection of nucleus on overlapped cells. The result shows that fine-tuned SSAE+SMC has significantly increased the accuracy of nucleus detection. The best accuracy achieves 0.876 on 50 × 50 window size.
KW - Automatic nucleus detection
KW - Deep learning
KW - Pap smear image
KW - Sliding window operation
KW - Softmax classifier.
KW - Stacked sparse autoencoder
UR - http://www.scopus.com/inward/record.url?scp=85039036657&partnerID=8YFLogxK
U2 - 10.1145/3127942.3127946
DO - 10.1145/3127942.3127946
M3 - Conference contribution
AN - SCOPUS:85039036657
T3 - ACM International Conference Proceeding Series
SP - 9
EP - 13
BT - Proceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
PB - Association for Computing Machinery
T2 - 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
Y2 - 10 August 2017 through 13 August 2017
ER -