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Image Processing and Deep Convolutional Neural Network Method for Automated Malaria Parasite Detection in Thin Blood

Kavita Kumari^1,2, Taruna Kaura³, Abhishek Mewara³

¹Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.

Diagnostics (Basel, Switzerland)

|July 15, 2026

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This study developed a computer vision algorithm to detect malaria parasites in red blood cells (RBCs) from blood slide images. Transfer learning models, particularly VGG19, achieved high accuracy, showing potential for automated malaria screening.

Area of Science:

Medical Diagnostics
Computational Biology
Image Analysis

Background:

Malaria, caused by Plasmodium species, is a significant global health threat.
Current microscopic diagnosis relies on manual blood slide examination, which is time-consuming and prone to observer variability.
There is a need for objective, efficient, and accurate malaria diagnostic tools.

Purpose of the Study:

To develop and evaluate a computer vision algorithm for automated malaria parasite detection and classification.
To compare the performance of various deep learning models, including transfer learning architectures, for identifying infected red blood cells (RBCs).
To assess the potential of computational approaches as an aid in malaria screening.

Main Methods:

Digital image processing techniques, including watershed transform and connected component labelling, were used for RBC segmentation.

Keywords:

computer-aided diagnosis convolutional neural network densely connected convolutional network polymerase chain reaction rapid diagnostic test visual geometry group network

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Convolutional Neural Networks (CNNs), including transfer learning models (VGG16, VGG19, DenseNet121, InceptionV3) and a custom CNN, were employed for classification.

The models were trained and tested on segmented RBC images (2422 training, 692 testing).

Main Results:

The VGG19 model achieved the highest classification accuracy of 99.57% for malaria parasite detection.
The custom CNN model also demonstrated strong performance with 99.14% accuracy.
Transfer learning models proved highly effective in automatically extracting features for accurate classification.

Conclusions:

Transfer learning models, especially VGG19, exhibit high accuracy for classifying malaria parasites in blood smear images.
The developed algorithm shows promise as a computational tool to assist in malaria diagnosis.
This approach can complement existing diagnostic methods, potentially improving screening efficiency and accuracy.