Sıtma Hastalığının Otomatik Tespiti için EfficientNet Tabanlı Segmentasyon Modellerinin Performans Analizi

Abstract

Malaria is a disease caused by the Plasmodium parasite, which is common in the tropics. The traditional methods commonly used to diagnose malaria, one of the world's deadliest diseases, are microscopic diagnostic methods in which blood samples taken from suspected individuals are manually examined, or rapid diagnostic tests that are sensitive to human errors. These processes are inexpensive, but experienced and qualified clinicians are needed. Due to this shortcoming, modern diagnostic tools are crucial in the struggle against the disease. In this study, an approach based on deep learning methods was used, which offers beneficial solutions in the diagnosis of disease from medical images. In the proposed approach, U-Net, Pyramid Scene Parsing Network, LinkNet, and Feature Pyramid Network segmentation methods were modified with 8 different pre-trained variants of the EfficientNet deep learning model to obtain improved models. In the malaria segmentation performed with these models, the highest Dice score of 91.50% was achieved in the use of the U-Net model with EfficientNetB6. This model offers a faster and more robust solution to detecting parasites compared to traditional methods.