SEGMENTASI SEMANTIK MULTICLASS PADA CITRA PRA-KANKER SERVIKS MENGGUNAKAN DEEP LEARNING

AKHYAR, MUHAMMAD REZKY HAMESI and Nurmaini, Siti (2023) SEGMENTASI SEMANTIK MULTICLASS PADA CITRA PRA-KANKER SERVIKS MENGGUNAKAN DEEP LEARNING. Undergraduate thesis, Sriwijaya University.

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Abstract

Artificial Intelligence (AI) technology in the field of Computer Vision (CV) can be utilized to detect cervical cancer using the Image Segmentation approach. With the advancement of technology, image segmentation processes can be implemented using Deep Learning (DL) models. This research utilizes the U-Net architecture with backbones for pre-cervical cancer semantic image segmentation. There are 12 backbones combined with the U-Net architecture: Vgg16, Vgg19, ResNet50, ResNext50, EfficientNetb7, InceptionResNetv2, DenseNet201, Inceptionv3, Mobilenetv2, Se-ResNet50, SE-ResNext50, and SE-Net154. The best performance is achieved by the U-Net model using the SENet154 backbone. The evaluation results of the U-Net model with the SENet154 backbone on the metrics of Pixel Accuracy, Intersection Over Union (IoU), and Dice Coefficient are 81.82%, 71.69%, and 82.29%, respectively.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Computer Vision
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Muhammad Rezky Hamesi Akhyar
Date Deposited:	01 Aug 2023 08:31
Last Modified:	01 Aug 2023 08:31
URI:	http://repository.unsri.ac.id/id/eprint/124963

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