Peningkatan Akurasi Deteksi Penyakit Malaria Menggunakan Transfer Learning pada Arsitektur CNN
DOI:
https://doi.org/10.35889/jutisi.v14i3.3346Keywords:
Malaria, Klasifikasi citra, VGG16, ROC-AUC, McNemarAbstract
Malaria is a disease that requires fast and accurate diagnosis. This study compares three CNN architectures VGG16, ResNet50, and InceptionV3 for malaria peripheral blood smear classification using the public NIH dataset. The pipeline includes standardized preprocessing, moderate augmentation, and transfer learning with early stopping (monitoring val_recall). Evaluation on a stratified test set covers accuracy, precision, recall, F1, ROC-AUC, PR-AP, confusion matrix, and paired statistics (McNemar). VGG16 yields the best performance at the 0.50 threshold (AUC 0.9833; AP 0.9846; Recall 0.8955; F1 0.9308) and significantly outperforms InceptionV3 (X2(1)=111.06; p<1x110-24). Bootstrap uncertainty (1.000 resamples) gives Recall mean 0.8956 (95% CI 0.8843–0.9077) and F1 mean 0.9309 (95% CI 0.9239–0.9377). Findings support a VGG16-based model as a feasible pre-screening module in resource-constrained settings, emphasizing sensitivity to reduce false negatives.
Keywords: Malaria; Image classification; VGG16; RO-AUC; McNemar
Abstrak
Malaria merupakan penyakit yang memerlukan diagnosis cepat dan akurat. Penelitian ini membandingkan tiga arsitektur CNN VGG16, ResNet50, dan InceptionV3 untuk klasifikasi citra apusan darah tepi malaria berbasis dataset publik NIH. Pipeline meliputi praproses terstandar, augmentasi moderat, dan transfer learning dengan early stopping (monitor val_recall). Evaluasi dilakukan pada himpunan uji terstratifikasi mengukur akurasi, presisi, recall, F1, ROC-AUC, PR-AP, confusion matrix, serta uji statistik berpasangan (McNemar). VGG16 menunjukkan kinerja terbaik pada ambang 0.50 (AUC 0.9833; AP 0.9846; Recall 0.8955; F1 0.9308) dan unggul signifikan atas InceptionV3(X2(1)=111.06; p<1x110-24). Estimasi ketidakpastian berbasis bootstrap (1.000 ulangan) menghasilkan Recall mean 0.8956 (CI95% 0.8843–0.9077) dan F1 mean 0.9309 (CI95% 0.9239–0.9377). Temuan ini mendukung model berbasis VGG16 sebagai modul pra-skrining otomatis di lingkungan berdaya komputasi terbatas, dengan penekanan pada sensitivitas untuk meminimalkan salah-negatif.
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