Skripsi

SISTEM PENDETEKSIAN PENYAKIT PNEUMONIA PADA CITRA RONTGEN DADA MENGGUNAKAN METODE CNN DENGAN ARSITEKTUR RETINANET

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Penilaian

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Pneumonia is one of the lung diseases that is often identified through chest X-ray images, but manual diagnosis requires high time and skill. Therefore, this study aims to improve the efficiency and accuracy of pneumonia diagnosis through the application of deep learning technology. The CNN method was chosen due to its ability to automatically extract features from medical images. RetinaNet, as an object detection model architecture, was chosen to improve the accuracy of disease localization in X-ray images. The training data used comes from a set of chest X-ray image data that has been annotated with a pneumonia label. The experimental results show that the developed system is able to detect pneumonia disease in chest X-ray images with mAP scores of 0.95 with IoU Threshold 0.3 and 0.83 with IoU Threshold 0.5. The application of RetinaNet architecture to CNN contributes significantly in improving the accuracy of disease detection. Thus, this system is expected to be a tool for medical personnel in supporting the rapid and accurate diagnosis of pneumonia based on chest X-ray images.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407001849	T141774	T1417742024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1417742024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer., 2024

Collation

xiv, VI-2 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

-

Subject(s)

Sistem Pakar
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

UIN FARRAH

Other version/related

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