Malaria Parasite Enumeration and Classification Using Convolutional Neural Networking

Preethi, S.; Devi, B. Aruna; Prasannadevi, V.

doi:10.1007/978-3-030-60265-9_14

Cited by 3 publications

(1 citation statement)

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“…The proposed technique outcomes are compared to existing works such as [64][65][66][67]. The capsule network has been utilized for discrimination among infected/uninfected cells of malaria with 96.9% accuracy [67].…”

Section: Experiment#2: Classification Outcomes Using the Quantum-convolutional Modelmentioning

confidence: 99%

Malaria Parasite Detection Using a Quantum-Convolutional Network

Amin¹,

Anjum²,

Sharif³

et al. 2022

Computers, Materials &Amp; Continua

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Malaria is a severe illness triggered by parasites that spreads via mosquito bites. In underdeveloped nations, malaria is one of the top causes of mortality, and it is mainly diagnosed through microscopy. Computer-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected groups. Therefore, in this study, we present a new idea based on the ensemble quantum-classical framework for malaria classification. The methods comprise three core steps: localization, segmentation, and classification. In the first core step, an improved FRCNN model is proposed for the localization of the infected malaria cells. Then, the RGB localized images were converted into YCbCr channels to normalize the image intensity values. Subsequently, the actual lesion region was segmented using a histogram-based color thresholding approach. The segmented images were employed for classification in two different ways. In the first method, a CNN model is developed by the selection of optimum layers after extensive experimentation, and the final computed feature vector is passed to the softmax layer for classification of the infection/non-infection of the microscopic malaria images. Second, a quantum-convolutional model is employed for informative feature extraction from microscopic malaria images, and the extracted feature vectors are supplied to the softmax layer for classification. Finally, classification results were analyzed from two different models and concluded that the quantum-convolutional model achieved maximum accuracy as compared to CNN. The proposed models attain a precision rate greater than 90%, thereby proving that these models performed better than the existing models.

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