Automated Classification and Detection of Malaria Cell Using Computer Vision

Chatterjee, Subhrasankar; Majumder, Pritha

doi:10.1007/978-981-15-7834-2_45

Cited by 3 publications

(3 citation statements)

References 5 publications

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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%

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

confidence: 99%

“…However, the proposed quantum-convolutional model achieved 100% accuracy. 2021 95 [66] 2021 97.98 [67] 2020 96.9 Proposed model 100…”

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

View full text Add to dashboard Cite

show abstract

“…In order to promote the widespread use of malaria classification models and address associated issues, it is essential to improve existing models by reducing the number of parameters, shortening processing times, and enhancing classification performance. This study highlights these challenges and proposes a novel and effective solution [31,32].…”

mentioning

confidence: 99%

Malaria Parasite Classification from RBC Smears Using Lightweight Parallel Depthwise Separable CNN and Ridge Regression ELM by Integrating SHAP Techniques

Ahamed,

Nahiduzzaman,

Ayari

et al. 2023

Preprint

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Malaria is a significant health concern worldwide, and early detection and accurate classification are essential for better treatment. This study proposes a new method that combines a lightweight parallel depth-wise separable convolutional neural network (LPDCNN) with a hybrid ridge regression extreme learning machine (RELM) to classify images of infected and uninfected patients' red blood cells (RBCs). We include a hybrid pre-processing step that uses contrast-limited adaptive histogram equalization (CLAHE) and Dilation operation to enhance image quality, reduce cell noise, and improve visual acuity. The LPDCNN extracts discriminative features efficiently with only 0.36 million parameters and 8 layers, minimizing computational complexity. The hybrid RELM model improves classification performance and replaces the traditional pseudoinverse of the ELM approach. Rigorous five-fold cross-validation (CV) for binary class classifications shows that the framework has impressive average precision, recall, f1, accuracy, and AUC scores of 99.86±0.08%, 99.88±0.084%, 99.84±0.089%, 99.85±0.071%, and 99.96±0.037%, respectively, surpassing state-of-the-art (SOTA) models. The proposed framework is exceptionally efficient, with an average training and testing time of 0.1376 and 0.00255 seconds, respectively. Additionally, the framework is integrated SHAP (Shapley Additive Explanations) to enhance interpretability, providing valuable insights into decision-making and instilling confidence in malaria diagnosis for real-world applications. This comprehensive approach holds promise in improving malaria diagnosis and patient outcomes worldwide.

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Malaria Parasite Diagnosis Using Computational Techniques: A Comprehensive Review

Mustafa

Alquran

Aihsan

et al. 2021

J. Phys.: Conf. Ser.

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Malaria is a very serious disease that caused by the transmitted of parasites through the bites of infected Anopheles mosquito. Malaria death cases can be reduced and prevented through early diagnosis and prompt treatment. A fast and easy-to-use method, with high performance is required to differentiate malaria from non-malarial fevers. Manual examination of blood smears is currently the gold standard, but it is time-consuming, labour-intensive, requires skilled microscopists and the sensitivity of the method depends heavily on the skills of the microscopist. Currently, microscopy-based diagnosis remains the most widely used approach for malaria diagnosis. The development of automated malaria detection techniques is still a field of interest. Automated detection is faster and high accuracy compared to the traditional technique using microscopy. This paper presents an exhaustive review of these studies and suggests a direction for future developments of the malaria detection techniques. This paper analysis of three popular computational approaches which is k-mean clustering, neural network, and morphological approach was presented. Based on overall performance, many research proposed based on the morphological approach in order to detect malaria.

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Automated Classification and Detection of Malaria Cell Using Computer Vision

Cited by 3 publications

References 5 publications

Malaria Parasite Detection Using a Quantum-Convolutional Network

Malaria Parasite Detection Using a Quantum-Convolutional Network

Malaria Parasite Classification from RBC Smears Using Lightweight Parallel Depthwise Separable CNN and Ridge Regression ELM by Integrating SHAP Techniques

Malaria Parasite Diagnosis Using Computational Techniques: A Comprehensive Review

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