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What new methods are being developed to diagnose malaria? 

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New methods being developed to diagnose malaria include the use of artificial intelligence (AI) and deep learning models . These models, such as CNN, ResNet50, and VGG19, have shown promising results in recognizing the Plasmodium parasite in thick blood smear images . Additionally, molecular-based diagnostics such as polymerase chain reaction (PCR) and loop-mediated isothermal amplification (LAMP) are being explored for their high sensitivity even at low parasite density . Other approaches include the integration of denoising and image segmentation techniques with Generative Adversarial Network (GAN) as a data augmentation technique . Nanotechnology is also being investigated as a potential tool for malaria detection . These new methods aim to improve the speed, accuracy, and accessibility of malaria diagnosis, addressing the limitations of traditional techniques such as microscopy .

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The paper discusses the use of deep learning technologies, such as convolutional neural networks (CNNs) and image processing, for automated diagnosis of malaria. It specifically mentions the use of transfer learning and fine-tuning techniques to enhance diagnostic accuracy.
The paper discusses the need for effective diagnostic methods for malaria but does not provide specific information about new methods being developed.
Open accessJournal ArticleDOI
The paper discusses the challenges in malaria diagnostics and highlights current interventions such as the amalgamation of different techniques, nanotechnology, and artificial intelligence to improve malaria detection.
The paper discusses the use of deep learning models, such as CNN, ResNet50, and VGG19, for diagnosing malaria by recognizing the Plasmodium parasite in thick blood smear images.

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