Classification Of Lung Disease Images Using EfficientNetB2 Model With Random Sampling
DOI:
https://doi.org/10.56480/jln.v5i3.1650
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EfficientNet B2, Random Sampling, Lung Disease, Class ImbalanceAbstract
The rapid advancement of deep learning in medical imaging has significantly improved lung disease diagnosis, with CNNs like EfficientNet showing strong performance on chest X-ray analysis. However, class imbalance remains a challenge, often reducing model accuracy. This study examines the impact of random oversampling compared to original and undersampled data in classifying lung diseases using EfficientNet-B2. Emphasizing its simplicity, the study evaluates whether random oversampling can match more complex methods like SMOTE. Through systematic data collection, preprocessing, and model training, performance is assessed using accuracy, precision, recall, and F1-score. Results show EfficientNet-B2 consistently outperforms MobileNetV3-Large across all sampling methods, with random oversampling achieving the best results—training accuracy of 99.61% and testing accuracy of 93.65% under a 70:30 split. While oversampling proves most effective, method selection should consider specific application needs, resource constraints, and deployment scale to ensure reliable diagnostic outcomes.
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Copyright (c) 2025 Rengga Yogie Febrianto, I Gede Susrama Mas Diyasa, Eka Prakarsa Mandyartha
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