Comparative Evaluation of Custom Convolutional Neural Networks and EfficientNet-B3 for Malaria Cell Image Classification: Impact of Targeted Data Augmentation on Model Performance
نویسندگان
1 Department of Anesthesia, School of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2 Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
3 Department of Midwifery, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4 Department of Health Information Management and Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
5 Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
6 Institute of Biophysics and Informatics, First Faculty of Medicine, Charles University, Prague, Czech Republic.
doi
10.61882/ijbc.17.3.62چکیده
Background: Malaria diagnosis with thin blood smears remains labor-intensive and relies on the operator. Deep learning could enable accurate automation. Objective: Compare four convolutional approaches for classifying parasitized versus uninfected erythrocytes and to evaluate whether targeted image-quality augmentations enhance performance. Materials and Methods: We used the balanced NIH/Kaggle dataset, which ...
