Abstract:
Malaria diagnosis is a severe difficulty in preserving human health, particularly in developing
nations. It is essential if correct treatment and control measures are to be instituted and
implemented. Thus, this research focuses on A Two-Stage Deep Learning Approach with
Image-Tiling for Malaria Diagnosis from Microscopic Blood Smears. Due to the necessary
image downscaling, traditional deep-learning models for malaria screening in high-resolution
microscopic images often lose crucial spatial details. This research introduces an image-tiling
technique with overlaps to preserve spatial resolution and enhance detection accuracy.
Comparing YOLOv9 and RT-DETR, the study aims to identify which models are the most
suitable for accurate malaria detection in blood smears, focusing on eliminating false positives
and enhancing the model's reliability. This method represents a significant development since
it preserves the integrity of high-resolution data throughout the detection procedure. The
researchers propose a thorough two-stage detection methodology employing sophisticated
tiling and refined object identification algorithms to improve malaria diagnosis. Images are
divided into 640x640 tiles, and overlapping between tiles is 30% so as not to lose spatial
information. The first stage utilizes the YOLOv9 algorithm to identify malaria parasites with
an accuracy of 0.896. The MobileViT model subsequently enhances these detections by
minimizing false positives, attaining a validation accuracy of 0.9833. After establishing
malaria's existence, the procedure advances to the second stage, where thin blood smears are
examined to identify kinds of malaria parasites using identical models, achieving detection
accuracies of 0.931 and classification accuracies of 0.929188. This two-part approach
significantly improves diagnostic accuracy and decreases the time needed to arrive at a
diagnosis. The culmination of this method is integrated into a fully autonomous desktop
application designed to seamlessly mesh with existing healthcare systems, dramatically
reducing diagnostic response times. This novel application enhances patient quality by
providing quick and accurate malaria diagnosis. It enhances the possibilities of deploying deep
learning models in medical imaging, which promises to transform patient outcomes in several
medical fields.
