TY - GEN
T1 - Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening
AU - Kudva, Vidya
AU - Prasad, Keerthana
AU - Guruvare, Shyamala
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Automated analysis of digital cervix images acquired during Visual Inspection with Acetic acid (VIA) is found to be of great help to aid the physicians to diagnose cervical cancer. Traditional classification methods require many features to distinguish between normal and abnormal cervix. Selection of distinct visual features which well represent the data and at the same time are capable of performing discriminative learning is complex. This problem can be overcome using deep learning approaches. Transfer learning is one of the deep learning approaches, which facilitates the use of a pre-trained network for a specific problem at hand. This paper presents a transfer learning using AlexNet, which is a pre-trained convolutional neural network, for classification of the cervix images into two classes namely negative and positive. This study used 2198 cervix images with 1090 belonging to negative class and 1108 to positive class. Our experiment using AlexNet for transfer learning achieved an accuracy of 0.934.
AB - Automated analysis of digital cervix images acquired during Visual Inspection with Acetic acid (VIA) is found to be of great help to aid the physicians to diagnose cervical cancer. Traditional classification methods require many features to distinguish between normal and abnormal cervix. Selection of distinct visual features which well represent the data and at the same time are capable of performing discriminative learning is complex. This problem can be overcome using deep learning approaches. Transfer learning is one of the deep learning approaches, which facilitates the use of a pre-trained network for a specific problem at hand. This paper presents a transfer learning using AlexNet, which is a pre-trained convolutional neural network, for classification of the cervix images into two classes namely negative and positive. This study used 2198 cervix images with 1090 belonging to negative class and 1108 to positive class. Our experiment using AlexNet for transfer learning achieved an accuracy of 0.934.
UR - http://www.scopus.com/inward/record.url?scp=85076974336&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076974336&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-0626-0_25
DO - 10.1007/978-981-15-0626-0_25
M3 - Conference contribution
AN - SCOPUS:85076974336
SN - 9789811506253
T3 - Lecture Notes in Electrical Engineering
SP - 299
EP - 312
BT - Advances in Communication, Signal Processing, VLSI, and Embedded Systems - Select Proceedings of VSPICE 2019
A2 - Kalya, Shubhakar
A2 - Kulkarni, Muralidhar
A2 - Shivaprakasha, K. S.
PB - Springer Paris
T2 - International Conference on VLSI, Signal Processing, Power Systems, Illumination and Lighting Control, Communication and Embedded Systems, VSPICE 2019
Y2 - 23 May 2019 through 24 May 2019
ER -
