Microscopia Automatizada e Inteligente com Deep Learning para Detecção de Malária

Abstract

This study presents experimental results on the use of YOLO-based Regional Convolutional Neural Networks for the detection of malaria-causing protozoa in microscopic images. The analysis of the experimental results highlighted YOLOv7 X as the benchmark solution, achieving an F-Score of 80.24%, which validates the proposed strategy on a realistic dataset and contributes to the development of Automated and Intelligent Microscopy.