Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening

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

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Communication, Signal Processing, VLSI, and Embedded Systems - Select Proceedings of VSPICE 2019
EditorsShubhakar Kalya, Muralidhar Kulkarni, K. S. Shivaprakasha
PublisherSpringer Paris
Pages299-312
Number of pages14
ISBN (Print)9789811506253
DOIs
Publication statusPublished - 01-01-2020
EventInternational Conference on VLSI, Signal Processing, Power Systems, Illumination and Lighting Control, Communication and Embedded Systems, VSPICE 2019 - Deralakatte, India
Duration: 23-05-201924-05-2019

Publication series

NameLecture Notes in Electrical Engineering
Volume614
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on VLSI, Signal Processing, Power Systems, Illumination and Lighting Control, Communication and Embedded Systems, VSPICE 2019
CountryIndia
CityDeralakatte
Period23-05-1924-05-19

Fingerprint

Screening
Acetic acid
Inspection
Neural networks
Experiments
Deep learning

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Cite this

Kudva, V., Prasad, K., & Guruvare, S. (2020). Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening. In S. Kalya, M. Kulkarni, & K. S. Shivaprakasha (Eds.), Advances in Communication, Signal Processing, VLSI, and Embedded Systems - Select Proceedings of VSPICE 2019 (pp. 299-312). (Lecture Notes in Electrical Engineering; Vol. 614). Springer Paris. https://doi.org/10.1007/978-981-15-0626-0_25
Kudva, Vidya ; Prasad, Keerthana ; Guruvare, Shyamala. / Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening. Advances in Communication, Signal Processing, VLSI, and Embedded Systems - Select Proceedings of VSPICE 2019. editor / Shubhakar Kalya ; Muralidhar Kulkarni ; K. S. Shivaprakasha. Springer Paris, 2020. pp. 299-312 (Lecture Notes in Electrical Engineering).
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abstract = "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.",
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Kudva, V, Prasad, K & Guruvare, S 2020, Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening. in S Kalya, M Kulkarni & KS Shivaprakasha (eds), Advances in Communication, Signal Processing, VLSI, and Embedded Systems - Select Proceedings of VSPICE 2019. Lecture Notes in Electrical Engineering, vol. 614, Springer Paris, pp. 299-312, International Conference on VLSI, Signal Processing, Power Systems, Illumination and Lighting Control, Communication and Embedded Systems, VSPICE 2019, Deralakatte, India, 23-05-19. https://doi.org/10.1007/978-981-15-0626-0_25

Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening. / Kudva, Vidya; Prasad, Keerthana; Guruvare, Shyamala.

Advances in Communication, Signal Processing, VLSI, and Embedded Systems - Select Proceedings of VSPICE 2019. ed. / Shubhakar Kalya; Muralidhar Kulkarni; K. S. Shivaprakasha. Springer Paris, 2020. p. 299-312 (Lecture Notes in Electrical Engineering; Vol. 614).

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

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Kudva V, Prasad K, Guruvare S. Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening. In Kalya S, Kulkarni M, Shivaprakasha KS, editors, Advances in Communication, Signal Processing, VLSI, and Embedded Systems - Select Proceedings of VSPICE 2019. Springer Paris. 2020. p. 299-312. (Lecture Notes in Electrical Engineering). https://doi.org/10.1007/978-981-15-0626-0_25