Deteksi Lesi Pra-Kanker Serviks Pada Citra Kolposkopi Menggunakan Convolutional Neural Network dengan Arsitektur YOLOv7

 Fatihani Nurqolbiah (Universitas Sriwijaya, Palembang, Indonesia)
 (*)Siti Nurmaini Mail (Universitas Sriwijaya, Palembang, Indonesia)
 Tommy Saputra (Universitas Sumatera Selatan, Palembang, Indonesia)

(*) Corresponding Author

Submitted: December 14, 2023; Published: December 29, 2023

Abstract

Pre-cancerous cervical lesions detection is crucial in the diagnosis and analysis of medical images. Because visual observations are weak, computer-based detection is needed. This research proposes a pre-cancerous cervical lesion detection model using a Convolutional Neural Network with the YOLOv7 architecture, capable of accurately detecting these lesions. The data used was 913 colposcopy image data from 200 cases. The dataset is divided into training and testing data, resulting in a detection model for pre-cancerous cervical lesions. The model achieves an mAP of 91.9%, precision of 87.7%, recall of 96%, and an F1-score of 93%. The study demonstrates that the performance of YOLOv7 indicates the model's ability to accurately detect pre-cancerous lesions in the cervix.

Keywords


Detection; Cervical Pre-Cancer; Colposcopy; Convolutional Neural Network; YOLOv7

Full Text:

PDF


Article Metrics

Abstract view : 275 times
PDF - 77 times

References

Ferlay J, Ervik M, Lam F. “Global cancer observatory: cancer today,” Lyon, France: International Agency for Research on Cancer, 2018

Novitasari DCR, Asyhar AH, Thohir M, Arifin AZ, Mu’jizah H, Foeady AZ. “Cervical cancer identification based texture analysis using glcm-kelm on colposcopy data,” Accessed International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2020.

A. Roihan, P. A. Sunarya, A. S. Rafika, "Pemanfaatan Machine Learning dalam Berbagai Bidang : Review paper," 2020, hal. 75–82.

J. A. Nichols, H. W. H. Chan, M. A. B. Baker, "Machine learning : applications of artificial intelligence to imaging and diagnosis," hal. 111–118, 2019.

A. Kumar, S. Srivastava, "Object Detection System Based on Convolution Using Single Shot Multi-Box Detector," Procedia Comput. Sci., vol. 171, hal. 2610–2617, 2020, doi: 10.1016/j.procs.2020.04.283.

J. Ker, L. Wang, J. Rao, dan T. Lim, "Deep Learning Applications in Medical Image Analysis," IEEE Access, vol. 6, hal. 9375–9379, 2017. doi: 10.1109/ACCESS.2017.2788044.

Li Y, Chen J, Xue P, Tang C, Chang J, Chu C, et al.. “Computer-aided cervical cancer diagnosis using time-lapsed colposcopic images,” IEEE Trans Med Imaging, 2020, doi: 10.1109/TMI.2020.2994778 .

Sato M, Horie K, Hara A, Miyamoto Y, Kurihara K, Tomio K, et al.. “Application of deep learning to the classification of images from colposcopy,” Oncol Lett , 2018, doi: 10.3892/ol.2018.7762.

S. K. Gautam et al., "Considerations for a PAP Smear Image Analysis System with CNN Features," arXiv:1806.0902, 2018

J. V. Burness, P. Oregon, F. Medicine, dan P. M. Hospital, "Cervical Colposcopy : Indications and Risk Assessment," 2020.

B. Bai et al., "Detection of cervical lesion region from colposcopic images based on feature reselection," Biomed. Signal Process. Control, vol. 57, hal. 101785, 2020. doi: 10.1016/j.bspc.2019.101785.

P. Guo et al., "Anatomical landmark segmentation in uterine cervix images using deep learning," Proc. SPIE, vol. 11318, hal. 32, 2020. doi: 10.1117/12.2549267.

M. Coskun et al., "An Overview of Popular Deep Learning Methods," Eur. J. Tech., vol. 7, no. 2, hal. 165–176, 2017. doi: 10.23884/ejt.2017.7.2.11.

P. Wang et al., "Automatic cell nuclei segmentation and classification of cervical Pap smear images," Biomed. Signal Process. Control, vol. 48, hal. 93–103, 2019. doi: 10.1016/j.bspc.2018.09.008.

V. Kudva, K. Prasad, dan S. Guruvare, "Automation of Detection of Cervical Cancer Using Convolutional Neural Networks," hal. 135–145, 2018.

S. Kiptoo, L. Nderu, dan L. Mutanu, "Automated Detection of Cervical Pre-Cancerous Lesions Using Regional-Based Convolutional Neural Network," 2020.

A. U. Rehman et al., "An Automatic Mass Screening System for Cervical Cancer Detection Based on Convolutional Neural Network," Math. Probl. Eng., 2020. doi: 10.1155/2020/4864835.

Y. Li et al., "Computer-aided Cervical Cancer Diagnosis using Time-lapsed Colposcopic Images," IEEE Trans. Med. Imaging, 2020. doi: 10.1109/TMI.2020.2994778.

Mueller JL, Lam CT, Dahl D, Asiedu MN, Krieger MS, Bellido-Fuentes Y, et al.. “Portable pocket colposcopy performs comparably to standard-of-care clinical colposcopy using acetic acid and lugol’s iodine as contrast mediators: an investigational study in peru”. BJOG . Int J Obstetric Gynaecol (2018) 125:1321–9. doi: 10.1111/1471-0528.15326

Q. Aini et al., "DETEKSI DAN PENGENALAN OBJEK DENGAN MODEL MACHINE LEARNING : MODEL YOLO," vol. 6, no. 2, hal. 192–199, 2021.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Deteksi Lesi Pra-Kanker Serviks Pada Citra Kolposkopi Menggunakan Convolutional Neural Network dengan Arsitektur YOLOv7

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Fatihani Nurqolbiah, Siti Nurmaini, Tommy Saputra

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Jurnal Sistem Komputer dan Informatika (JSON)
Dikelola oleh STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email : jurnal.json@gmail.com


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.