ANALISIS AKURASI MODEL MOBILENETV2 DALAM KLASIFIKASI CITRA X-RAY UNTUK DETEKSI KONDISI PARU-PARU

SYAKUROH, ABDAN and Monado, Fiber and Ariani, Menik (2025) ANALISIS AKURASI MODEL MOBILENETV2 DALAM KLASIFIKASI CITRA X-RAY UNTUK DETEKSI KONDISI PARU-PARU. Undergraduate thesis, Sriwijaya University.

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Abstract

This study aims to analyze the accuracy of the MobileNetV2 model in classifying chest X-ray images for detecting four pulmonary conditions: Normal, Pneumonia, Cardiomegaly, and Pneumothorax. The dataset consists of 12.539 X-ray images obtained from public repositories and has undergone preprocessing, augmentation, and class weighting to address data imbalance. The model was developed using transfer learning and fine-tuning on the final layers of MobileNetV2. Testing results indicate that the proposed model achieves an accuracy of 99,42%, precision of 98,87%, recall of 98,88%, and F1-score of 98,86%. All evaluation metrics exceed the minimum standard ≥90% for clinical application. These findings confirm that MobileNetV2 has strong potential as an automatic diagnostic tool based on X-ray images, thereby improving the effectiveness of early detection of lung diseases in clinical settings.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	MobileNetV2, Klasifikasi Citra X-Ray, Deteksi Penyakit Paru-Paru, Deep Learning, Diagnosis Otomatis
Subjects:	Q Science > QC Physics > QC474-496.9 Radiation physics (General)
Divisions:	08-Faculty of Mathematics and Natural Science > 45201-Physics (S1)
Depositing User:	Abdan Syakuroh
Date Deposited:	11 Jul 2025 07:14
Last Modified:	11 Jul 2025 07:14
URI:	http://repository.unsri.ac.id/id/eprint/177882

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