Ulinnuha, Villa (2023) Segmentasi Lung Infection dalam Citra CT Scan menggunakan Model Jaringan U-Net pada Deep Learning. (Lung Infection Segmentation in CT Scan Images Using U-Net Network Model in Deep Learning). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.
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Abstract
The year 2020 witnessed the emergence of COVID-19 as a global pandemic, necessitating effective diagnostic methods due to limitations in Reverse Transcription-Polymerase Chain Reaction (RT-PCR). This study explores the use of Computer Tomography (CT) imaging and the application of Convolutional Neural Network (CNN) techniques, specifically the U-Net model, for the segmentation of lung infections, focusing on Ground-Glass Opacity (GGO) and consolidation, which are indications of various lung infections. The main objective is to detect infected areas with a dataset of CT-Scan images using the U-Net method and evaluate the segmentation results. Using a dataset consisting of 350 original images and 350 images of lung infections, the U-Net model is trained, and its results are evaluated, producing a high level of accuracy during the training dataset process. The U-Net architecture, with its five-layer depth and convolutional mechanisms, demonstrates significant potential in accurately segmenting lung infections. The research methodology includes preprocessing steps involving cropping, normalization, and dataset splitting, followed by the training and evaluation of the U-Net model. The results of this study indicate a background accuracy of 0.98, while for the lung and infection segments, the accuracies are 0.47 and 0.17, respectively. This overfitting condition is attributed to the insufficient amount of training data.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | CNN (Convolutional Neural Network), Lung Infection, Segmentation, U-Net |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Teknik > Program Studi Teknik Informatika |
| Depositing User: | 1462000169 Villa Ulinnuha |
| Date Deposited: | 08 Jul 2024 03:22 |
| Last Modified: | 08 Jul 2024 03:22 |
| URI: | http://repository.untag-sby.ac.id/id/eprint/31072 |
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