CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space

Khan, Anik; Gupta, Kishor Datta; Venugopal, Deepak; Kumar, Nirman

doi:10.1109/ijcnn48605.2020.9206885

Cited by 18 publications

(14 citation statements)

References 29 publications

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“…A blood examination accompanied by an analysis of samples by a pathologist is critical [18]. Artificial intelligence assisting a pathologist in this diagnosis is a game changer for clinicians in terms of time savings [19]. In recent decades, many studies have been undertaken using statistical algorithms to offer premium solutions to promote interoperable health services for disease prevention [20].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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

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“…Rajaraman et al represented a deep neural ensemble model to detect malaria parasites in thin blood smear and achieved 99.32% accuracy [18]. Khan et al used Aggregated Laplacian coefficient and Inner Ring length to extract features from malaria cell images [19]. They achieved an 84% F1 score using the Random Forest Classifier (RFC) [19].…”

Section: Related Workmentioning

confidence: 99%

“…Khan et al used Aggregated Laplacian coefficient and Inner Ring length to extract features from malaria cell images [19]. They achieved an 84% F1 score using the Random Forest Classifier (RFC) [19]. Montalbo et al applied the transfer learning model (EfficientNETB0) and achieved 94.70% accuracy in detecting malaria parasites [20].…”

Section: Related Workmentioning

confidence: 99%

Malaria cell image classification by explainable artificial intelligence

Raihan

Nahid

2021

Health Technol.

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Malaria is one of the most common diseases in the world. It is caused by Plasmodium parasites and spreads among humans by the bite of female Anopheles mosquitoes. Only in 2019, children under the age of five accounted for 67% of all malaria deaths. The diagnosing process of malaria can be error-prone due to an inexperienced pathologist and may take a longer time. In this paper, we have presented a malaria detection framework based on wavelet packet 2d, Convolutional Neural Network. (CNN), and Whale Optimization Algorithm (WOA). We have extracted a global feature set using the wavelet packet 2D and CNN. Further, we removed noisy features and selected an effective feature using WOA and XGBoost. The selected feature set is almost half of the initial feature set. Additionally, we have used SHapley Additive exPlanations (SHAP) to interpret the trained model and assess the significance of each feature. Based on the selected feature set, the XGBoost algorithm provides accuracy, precision, recall, and F1 score of 94. 78%, 94. 39%, 95. 21%, and 94. 80% respectively.

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“…Khan et al used three machine learning (ML) models-logistic regression (LR), decision tree (DT), and random forest (RF)-to predict MPs from RBC images [19]. Firstly, they extracted the aggregated features from the cell images and achieved a high recall of 86% using RF.…”

Section: Introductionmentioning

confidence: 99%

Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images

Islam

Nahiduzzaman

Goni

et al. 2022

Sensors

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Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim’s blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even death. Microscopy is a familiar process for diagnosing malaria, collecting the victim’s blood samples, and counting the parasite and red blood cells. However, the microscopy process is time-consuming and can produce an erroneous result in some cases. With the recent success of machine learning and deep learning in medical diagnosis, it is quite possible to minimize diagnosis costs and improve overall detection accuracy compared with the traditional microscopy method. This paper proposes a multiheaded attention-based transformer model to diagnose the malaria parasite from blood cell images. To demonstrate the effectiveness of the proposed model, the gradient-weighted class activation map (Grad-CAM) technique was implemented to identify which parts of an image the proposed model paid much more attention to compared with the remaining parts by generating a heatmap image. The proposed model achieved a testing accuracy, precision, recall, f1-score, and AUC score of 96.41%, 96.99%, 95.88%, 96.44%, and 99.11%, respectively, for the original malaria parasite dataset and 99.25%, 99.08%, 99.42%, 99.25%, and 99.99%, respectively, for the modified dataset. Various hyperparameters were also finetuned to obtain optimum results, which were also compared with state-of-the-art (SOTA) methods for malaria parasite detection, and the proposed method outperformed the existing methods.

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CIDMP: Completely Interpretable Detection of Malaria Parasite in Red Blood Cells using Lower-dimensional Feature Space

Cited by 18 publications

References 29 publications

Malaria Parasite Detection Using a Quantum-Convolutional Network

Malaria Parasite Detection Using a Quantum-Convolutional Network

Malaria cell image classification by explainable artificial intelligence

Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images

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