Implementasi Model CNN ResNet50V2 untuk Klasifikasi Pneumonia pada Citra X-Ray
DOI:
https://doi.org/10.32664/smatika.v15i01.1538Kata Kunci:
CNN, Citra X-ray, Klasifikasi Pneumonia, ResNet50V2, Transfer LearningAbstrak
Pemanfaatan teknologi untuk membangun model yang dapat mengklasifikasi citra medis pneumonia secara otomatis sangat dibutuhkan untuk diagnosis dini. Penelitian ini bertujuan untuk mengimplementasikan model Convolutional Neural Network (CNN) dengan arsitektur ResNet50V2 yang telah terbukti memiliki akurasi tinggi dalam klasifikasi citra medis. Model ini mengadopsi arsitektur residual yang mendalam dan efisien, yang memfasilitasi pelatihan model yang lebih dalam tanpa mengalami masalah vanishing gradient. Penelitian ini melalui empat tahap utama: pengumpulan data citra X-ray pneumonia dan normal, pre-processing data (termasuk pembagian set, transformasi, dan augmentasi), pemodelan menggunakan CNN dengan tuning hyperparameter, dan evaluasi model. Evaluasi dilakukan dengan menggunakan metrik akurasi, F1-score, dan Confusion Matrix. Model CNN dengan ResNet50V2 sebagai backbone mencapai akurasi 97%, menunjukkan kinerja yang sangat baik dalam membedakan antara pneumonia dan normal meskipun terdapat sedikit kesalahan klasifikasi. Meskipun model ini menunjukkan hasil yang mengesankan, tantangan seperti potensi kesalahan klasifikasi pada kasus dengan citra yang tidak jelas atau ambigu tetap ada. Dibandingkan dengan pendekatan sebelumnya, model ini menawarkan keunggulan dalam akurasi dan efisiensi pemrosesan berkat penggunaan ResNet50V2 yang lebih dalam dan lebih canggih. Keunggulan ini diharapkan dapat meningkatkan ketepatan diagnosis otomatis dalam aplikasi medis di masa depan.
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