Malarial Diagnosis with Deep Learning and Image Processing Approaches

نویسندگان

چکیده

Malaria is a mosquito-borne disease that has killed an estimated half-a-million people worldwide since 2000. It may be time consuming and costly to conduct thorough laboratory testing for malaria, it also requires the skills of trained personnel. Additionally, human analysis might make mistakes. Integrating denoising image segmentation techniques with Generative Adversarial Network (GAN) as data augmentation technique can enhance performance diagnosis. Various deep learning models, such CNN, ResNet50, VGG19, recognising Plasmodium parasite in thick blood smear images have been used. The experimental results indicate VGG19 model performed best by achieving 98.46% compared other approaches. This study demonstrates potential artificial intelligence improve speed precision pathogen detection which more effective than manual analysis.

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ژورنال

عنوان ژورنال: International Journal on Recent and Innovation Trends in Computing and Communication

سال: 2023

ISSN: ['2321-8169']

DOI: https://doi.org/10.17762/ijritcc.v11i5s.6647