Abstract
- Early and accurate diagnosis of malaria is important for effective treatment and reduction of mortality rates. Traditional microscopy and manual cell counting methods are labor-intensive and prone to errors. This project introduces an innovative solution in the form of an automated cell counting system. The system is designed to efficiently identify and count red blood cells (RBCs), distinguishing between uninfected and malaria-infected cells with high precision. The proposed system will overcome the limitations of conventional methods by automating the counting process, with reduced processing time and minimized human error. The approach promises to deliver faster and more accurate diagnostic reports, which would facilitate early detection and treatment of malaria. The efficiency of the model will be judged based on the precision in the identification and enumeration of cells as well as its capability to provide diagnostic reports with a speed far more superior than that of the manual procedures. Finally, this automatic system will lead to an efficient diagnosis, improved patient care, and a further contribution to the fight against malaria. Key Words: Automated Diagnosis, Malaria, Red Blood Cell Counting, CNN, Deep Learning, VGG16.
Published Version
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