DETEKSI AREA LESI TERINFEKSI PADA KASUS PRA-KANKER SERVIKS MENGGUNAKAN PENDEKATAN SEMANTIK SEGMENTASI DAN FASTER-RCNN

AJDA, JARNA and Nurmaini, Siti (2022) DETEKSI AREA LESI TERINFEKSI PADA KASUS PRA-KANKER SERVIKS MENGGUNAKAN PENDEKATAN SEMANTIK SEGMENTASI DAN FASTER-RCNN. Undergraduate thesis, Sriwijaya University.

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Abstract

Cervical pre-cancer is the initial occurrence of cancer and referred to as cervical lesions and carcinoma (CA). Before the formation of pre-cancerous cells (lesions), abnormal cells appear on the cervix which is the prelude to cervical pre-cancer. The squamous columnar junction (SCJ) is a transitional area of the vaginal mucosa and cervical mucosa that looks like a distinct line between areas of columnar epithelium and squamous. A computer-assisted diagnostic system solves these problems, namely medical image segmentation and object detection. This study uses the Convolutional Neural Network (CNN) method, namely segmentation using U-Net architecture, V-Net, and detection using Faster R-CNN architecture. The initial data and augmented data resulted in 24 models from the U-Net architecture, 24 models from the V-Net architecture, and 12 models from the Faster R-CNN architecture using data from the best model results obtained from segmentation detection on the U-Net architecture. The best model from U-Net CA (II) which gets the highest evaluation results is 99.34% Pixel Accuracy, 94.16% Intersection Over Union (IoU), and 93.22% F1 Score. Lesion (II) 98.83% Pixel Accuracy, 94.48% Intersection Over Union (IoU), and 93.29% F1 Score. While the best model of the V-Net CA (II) architecture got the highest evaluation results of 99.32 Pixel Accuracy, 93.85 Intersection Over Union (IoU), and 93.03 F1 Score. Lesions (II) 99.00% Pixel Accuracy, 94.70% Intersection Over Union (IoU), and 94.09% F1 Score. The best model from Faster R-CNN gets the highest evaluation result of mAP with a value of 86.07%. Results Based on the experiment of segmentation using U-Net and V-Net, the U-Net segmentation result model is better than the model obtained from segmentation using V-Net.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Pra-kanker serviks, cervical carcinoma , Lesi, Sambungan skuamosa kolumnar, Augmentasi, Segmentasi, Deteksi, U-Net, V-Net, Faster R-CNN
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. Q Science > QA Mathematics > QA299.6-433 Analysis > Q334.A755 Artificial intelligence. Computational linguistics. Computer science. Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages R Medicine > R Medicine (General) > R856-857 Biomedical engineering. Electronics. Instrumentation > R857.M3.B56854 Biomedical materials. Stem cells--Therapeutic use. Regenerative medicine--Materials. TECHNOLOGY & ENGINEERING / Material Science. MEDICAL / Biotechnology T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Mrs. Jarna Ajda
Date Deposited:	12 Jul 2022 07:48
Last Modified:	12 Jul 2022 07:48
URI:	http://repository.unsri.ac.id/id/eprint/73717

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