Windra Swastika
ABSTRACT
Transfer learning has been used in computer vision research, including in the health sector. In the health sector, the input image is generally an x-ray image or a microscopic image. In this study, transfer learning for models that have been trained using CNN to detect malaria parasites in red blood cell images. The deep CNN pre-trained model uses 3 architectures, namely ResNet50V2, EfficientNetB0, and InceptionV3. For each architecture, experiments will be carried out and compare which architecture is better in detecting malaria parasites. Based on experiments conducted without fine tune, the accuracy ranges from 0.76 – 0.81 for ResNet50v2, 0.76 – 0.80 for EfficientNetB0, and 0.77 – 0.82 for InceptionV3.
The dataset is a collection of Blood Smear images which have two classes, uninfected and parasitized. The total number of datasets is 27,558, which is divided into two classes with the same number of different image sizes.
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Index Terms
- CNN Based Transfer Learning for Malaria Parasite Detection Using Thin-Blood Smear Images
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