Performance Analysis of Deep Learning Algorithms in Diagnosis of Malaria Disease

Diagnostics, Год журнала: 2023, Номер 13(3), С. 534 - 534

Опубликована: Фев. 1, 2023

Malaria is predominant in many subtropical nations with little health-monitoring infrastructure. To forecast malaria and condense the disease’s impact on population, time series prediction models are necessary. The conventional technique of detecting disease for certified technicians to examine blood smears visually parasite-infected RBC (red cells) underneath a microscope. This procedure ineffective, diagnosis depends individual performing test his/her experience. Automatic image identification systems based machine learning have previously been used diagnose smears. However, so far, practical performance has insufficient. In this paper, we made analysis deep algorithms disease. We Neural Network like CNN, MobileNetV2, ResNet50 perform analysis. dataset was extracted from National Institutes Health (NIH) website consisted 27,558 photos, including 13,780 parasitized cell images 13,778 uninfected images. conclusion, MobileNetV2 model outperformed by achieving an accuracy rate 97.06% better detection. Also, other metrics training testing loss, precision, recall, fi-score, ROC curve were calculated validate considered models.

Язык: Английский

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An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Parasites & Vectors, Год журнала: 2024, Номер 17(1)

Опубликована: Апрель 16, 2024

Abstract Background Malaria is a serious public health concern worldwide. Early and accurate diagnosis essential for controlling the disease’s spread avoiding severe complications. Manual examination of blood smear samples by skilled technicians time-consuming aspect conventional malaria toolbox. persists in many parts world, emphasising urgent need sophisticated automated diagnostic instruments to expedite identification infected cells, thereby facilitating timely treatment reducing risk disease transmission. This study aims introduce more lightweight quicker model—but with improved accuracy—for diagnosing using YOLOv4 (You Only Look Once v. 4) deep learning object detector. Methods The model modified direct layer pruning backbone replacement. primary objective removal individual analysis residual blocks within C3, C4 C5 (C3–C5) Res-block bodies architecture’s C3-C5 bodies. CSP-DarkNet53 simultaneously replaced enhanced feature extraction shallower ResNet50 network. performance metrics models are compared analysed. Results outperform original model. YOLOv4-RC3_4 pruned from C3 body achieves highest mean accuracy precision (mAP) 90.70%. mAP > 9% higher than that model, saving approximately 22% billion floating point operations (B-FLOPS) 23 MB size. findings indicate also performs better, an increase 9.27% detecting cells upon redundant layers CSP-DarkeNet53 backbone. Conclusions results this highlight use red cells. Pruning helps determine which contribute most least, respectively, model’s performance. Our method has potential revolutionise pave way novel learning-based bioinformatics solutions. Developing effective process will considerably global efforts combat debilitating disease. We have shown removing undesirable can reduce size its computational complexity without compromising precision. Graphical

Язык: Английский

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Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears

Sensors, Год журнала: 2025, Номер 25(2), С. 390 - 390

Опубликована: Янв. 10, 2025

Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle inconsistent stain quality, lighting variations, limited resources endemic regions, making manual detection time-intensive error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, malaria parasite classification. Using dataset of 1000 clinically diagnosed images, we applied feature extraction techniques, including histogram bins texture analysis gray level co-occurrence matrix (GLCM), alongside support vector machines (SVMs), assessment. Leukocyte employed optimal thresholding segmentation utility (OTSU) thresholding, binary masking, erosion, followed connected components algorithm. Parasite used high-intensity region selection adaptive bounding boxes, custom convolutional neural network (CNN) candidate identification. A second CNN classified parasites into trophozoites, schizonts, gametocytes. The achieved F1-score 95% 88.92% 82.10% detection. F1-score-a metric balancing precision (correctly identified positives) recall detected instances out actual positives)-is especially valuable assessing models imbalanced datasets. In stage classification, F1-scores 85% 88% 83% robust scalable that addresses critical challenges diagnosis integrating advanced assessment deep learning techniques system's adaptability to low-resource settings underscores its potential improve diagnostics globally.

Язык: Английский

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Application of hybrid capsule network model for malaria parasite detection on microscopic blood smear images

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Апрель 19, 2024

Язык: Английский

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Imaging malaria parasites across scales and time

Journal of Microscopy, Год журнала: 2025, Номер unknown

Опубликована: Янв. 3, 2025

Abstract The idea that disease is caused at the cellular level so fundamental to us we might forget critical role microscopy played in generating and developing this insight. Visually identifying diseased or infected cells lays foundation for any effort curb human pathology. Since discovery of Plasmodium ‐infected red blood cells, which cause malaria, has undergone an impressive development now literally resolving individual molecules. This review explores expansive field light microscopy, focusing on its application malaria research. Imaging technologies have transformed our understanding biological systems, yet navigating complex ever‐growing landscape techniques can be daunting. offers a guide researchers, especially those working by providing historical context as well practical advice selecting right imaging approach. advocates integrated methodology prioritises research question while considering key factors like sample preparation, fluorophore choice, modality, data analysis. In addition presenting seminal studies innovative applications highlights broad range topics, from traditional white advanced methods such superresolution time‐lapse imaging. It addresses emerging challenges including phototoxicity trade‐offs resolution speed, insights into future impact mix perspective, technological progress, guidance appeal novice microscopists alike. aims inspire researchers explore could enrich their studies, thus advancing through enhanced visual exploration parasite across scales time.

Язык: Английский

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Challenges and Ethical Considerations in Technological Interventions in the Management of Tropical Diseases

Health information science, Год журнала: 2025, Номер unknown, С. 309 - 327

Опубликована: Янв. 1, 2025

Язык: Английский

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Artificial Intelligence and Machine Learning in Tropical Disease Management

Health information science, Год журнала: 2025, Номер unknown, С. 155 - 182

Опубликована: Янв. 1, 2025

Язык: Английский

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Advancements and Challenges in Malaria Diagnostics

IntechOpen eBooks, Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

Diagnostic methods are vital for dealing with the global malaria burden and decreasing incidence. The diagnosis by microscopy is considered a gold standard; however, rapid diagnostic tests (RDTs) have become primary test in many malaria-endemic areas. RDTs advantages; gene deletion, poor sensitivity low parasite levels, cross-reactivity, prozone effect certain disadvantages. quantitative buffy coat (QBC), polymerase chain reaction (PCR), flow cytometry, loop-mediated isothermal amplification (LAMP), mass spectrometry disadvantages that limit their scale implications endemic Recently, based on artificial intelligence smartphone-based applications been developed, which can be implemented fields once high specificity achieved. In current scenario, deletion events Plasmodium falciparum created vacuum filled development of more advanced RDT.

Язык: Английский

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Evaluation of the malaria elimination programme in Muara Enim Regency: a qualitative study from Indonesia

Malaria Journal, Год журнала: 2024, Номер 23(1)

Опубликована: Фев. 12, 2024

Abstract Background Malaria remains an enduring public health concern in Indonesia, exacerbated by its equatorial climate that fosters the proliferation of Anopheles mosquitoes. This study seeks to assess performance malaria elimination programme comprehensively. Methods Between May and August 2022, a qualitative was conducted Muara Enim Regency, South Sumatra Province, involving 22 healthcare professionals from diverse backgrounds. These informants were strategically chosen for their pivotal roles providing profound insights into various facets programme. encompasses inputs such as human resources, budgetary allocation, infrastructural support; processes like case identification management, capacity enhancement, epidemiological surveillance, prevention measures, outbreak control, enhanced communication educational initiatives; and, notably, programme’s outcomes. Data collected through 3-h Focus Group Discussions (FGDs) divided two groups, each with 12 participants: managers. Additionally, in-depth interviews (IDIs) ten informants. Employing Input-Process-Output (IPO) model, this meticulously analysed system dynamics interventions’ efficacy. Results The unveiled many challenges during input phase, including absence entomologists shortage diagnostic tools. Despite these obstacles, it documented remarkable accomplishments output domain, marked significant advancements distribution mosquito nets successful implementation Early Warning System (EWS). adversities, has made substantial strides towards elimination. Conclusions Urgent action is imperative bolster effectiveness Key measures encompass augmenting entomologist workforce, optimizing resource ensuring stringent adherence regional regulations. Addressing concerns will enhance efficacy, yielding benefits. research substantially contributes Indonesia’s ongoing endeavours, furnishing actionable enhancement. Consequently, holds importance drive.

Язык: Английский

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Open-source toolkit for image acquisition and quality assessment of thin blood smears for malaria diagnosis

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 103, С. 107470 - 107470

Опубликована: Янв. 5, 2025

Язык: Английский

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