Effectively determining malaria parasitemia is a critical aspect in assisting clinicians to accurately determine the severity of the disease and provide quality treatment. Microscopy applied to thick smear blood smears is the de facto method for malaria parasitemia determination. However, manual quantification of parasitemia is time consuming, laborious and requires considerable trained expertise which is particularly inadequate in highly endemic and low resourced areas. This study presents an end-to-end approach for localisation and count of malaria parasites and white blood cells (WBCs) which aid in the effective determination of parasitemia; the quantitative content of parasites in the blood. On a dataset of slices of images of thick blood smears, we build models to analyse the obtained digital images. To improve model performance due to the limited size of the dataset, data augmentation was applied. Our preliminary results show that our deep learning approach reliably detects and returns a count of malaria parasites and WBCs with a high precision and recall. We also evaluate our system against human experts and results indicate a strong correlation between our deep learning model counts and the manual expert counts (p=0.998 for parasites, p=0.987 for WBCs). This approach could potentially be applied to support malaria parasitemia determination especially in settings that lack sufficient Microscopists.
Indonesia holds the second-highest-ranking country for the highest number of malaria cases in Southeast Asia. A different malaria parasite semantic segmentation technique based on a deep learning approach is an alternative to reduce the limitations of traditional methods. However, the main problem of the semantic segmentation technique is raised since large parasites are dominant, and the tiny parasites are suppressed. In addition, the amount and variance of data are important influences in establishing their models. In this study, we conduct two contributions. First, we collect 559 microscopi…
Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）；Howard Hughes Medical Institute（霍华德·休斯医学研究所）
Global eradication of malaria depends on the development of drugs effective against the silent, yet obligate liver stage of the disease. The gold standard in drug development remains microscopic imaging of liver stage parasites in in vitro cell culture models. Image analysis presents a major bottleneck in this pipeline since the parasite has significant variability in size, shape, and density in these models. As with other highly variable datasets, traditional segmentation models have poor generalizability as they rely on hand-crafted features; thus, manual annotation of liver stage malaria im…
Predicting if red blood cells (RBC) are infected with the malaria parasite is an important problem in Pathology. Recently, supervised machine learning approaches have been used for this problem, and they have had reasonable success. In particular, state-of-the-art methods such as Convolutional Neural Networks automatically extract increasingly complex feature hierarchies from the image pixels. While such generalized automatic feature extraction methods have significantly reduced the burden of feature engineering in many domains, for niche tasks such as the one we consider in this paper, they r…
Malaria is considered one of the deadliest diseases in today world which causes thousands of deaths per year. The parasites responsible for malaria are scientifically known as Plasmodium which infects the red blood cells in human beings. The parasites are transmitted by a female class of mosquitos known as Anopheles. The diagnosis of malaria requires identification and manual counting of parasitized cells by medical practitioners in microscopic blood smears. Due to the unavailability of resources, its diagnostic accuracy is largely affected by large scale screening. State of the art Computer-a…
Malaria is mosquito-borne blood disease caused by parasites of the genus Plasmodium. Conventional diagnostic tool for malaria is the examination of stained blood cell of patient in microscope. The blood to be tested is placed in a slide and is observed under a microscope to count the number of infected RBC. An expert technician is involved in the examination of the slide with intense visual and mental concentration. This is tiresome and time consuming process. In this paper, we construct a new mage processing system for detection and quantification of plasmodium parasites in blood smear slide,…
North South University（北南大学）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Military Institute of Science and Technology（军事科学技术学院）
Malaria is a female anopheles mosquito-bite inflicted life-threatening disease which is considered endemic in many parts of the world. This article focuses on improving malaria detection from patches segmented from microscopic images of red blood cell smears by introducing a deep convolutional neural network. Compared to the traditional methods that use tedious hand engineering feature extraction, the proposed method uses deep learning in an end-to-end arrangement that performs both feature extraction and classification directly from the raw segmented patches of the red blood smears. The datas…
Information Technology University（信息技术大学）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Chughtai Institute of Pathology（楚格泰病理研究所）；Ittefaq hospital（伊特法克医院）
Malaria microscopy, microscopic examination of stained blood slides to detect parasite Plasmodium, is considered to be a gold-standard for detecting life-threatening disease malaria. Detecting the plasmodium parasite requires a skilled examiner and may take up to 10 to 15 minutes to completely go through the whole slide. Due to a lack of skilled medical professionals in the underdeveloped or resource deficient regions, many cases go misdiagnosed; resulting in unavoidable complications and/or undue medication. We propose to complement the medical professionals by creating a deep learning-based …
Leading University（领先大学）；Clemson University（克莱姆森大学）
We apply convolutional neural networks to identify between malaria infected and non-infected segmented cells from the thin blood smear slide images. We optimize our model to find over 95% accuracy in malaria cell detection. We also apply Canny image processing to reduce training file size while maintaining comparable accuracy (~ 94%).
Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears
MyC；APHP Paris, La Pitié-Salpêtrière, Sorbonne Université（APHP巴黎，拉皮蒂埃-萨尔佩特里耶，索邦大学）；CHU of Montpellier, University of Montpellier（蒙彼利埃大学附属医院，蒙彼利埃大学）；CHU Campus, Ministère de la Santé et de l'hygiène Publique（CHU校区，卫生与公共卫生部）；CHU Sylvanus Olympio, Lomé, Togo（索维拉尼奥利奥医院，洛美，多哥）；Faculté des Sciences de la Santé, Université de Lomé（健康科学学院，洛美大学）
According to the World Health Organization(WHO), malaria is estimated to have killed 627,000 people and infected over 241 million people in 2020 alone, a 12% increase from 2019. Microscopic diagnosis of blood cells is the standard testing procedure to diagnose malaria. However, this style of diagnosis is expensive, time-consuming, and greatly subjective to human error, especially in developing nations that lack well-trained personnel to perform high-quality microscopy examinations. This paper proposes Mass-AI-Scope (MAIScope): a novel, low-cost, portable device that can take microscopic images…
Intellectual Ventures/Global Good Research（高智公司/全球好研究部）；IBM（国际商业机器公司）；Arizona State U.（亚利桑那州立大学）；Creative Creek LLC（创意溪有限责任公司）；LSHTM（伦敦卫生与热带医学院）；SMRU（湄公河疟疾研究组）；UPCH（秘鲁卡耶塔诺·埃雷迪亚大学）；HTD（医院热带病科（英国伦敦））；Amref（非洲医学研究基金会）；WWARN（世界抗疟耐药性监测网络）；Kemri（肯尼亚医学研究所）；U. Lagos（拉各斯大学）；DSMA（国防医学科学院（缅甸））
Malaria is a life-threatening disease affecting millions. Microscopy-based assessment of thin blood films is a standard method to (i) determine malaria species and (ii) quantitate high-parasitemia infections. Full automation of malaria microscopy by machine learning (ML) is a challenging task because field-prepared slides vary widely in quality and presentation, and artifacts often heavily outnumber relatively rare parasites. In this work, we describe a complete, fully-automated framework for thin film malaria analysis that applies ML methods, including convolutional neural nets (CNNs), traine…
Malaria, a life-threatening disease, infects millions of people every year throughout the world demanding faster diagnosis for proper treatment before any damages occur. In this paper, an end-to-end deep learning-based approach is proposed for faster diagnosis of malaria from thin blood smear images by making efficient optimizations of features extracted from diversified receptive fields. Firstly, an efficient, highly scalable deep neural network, named as DilationNet, is proposed that incorporates features from a large spectrum by varying dilation rates of convolutions to extract features fro…
Malaria is a serious disease caused by the Plasmodium parasite that transmitted through the bite of a female Anopheles mosquito and invades human erythrocytes. Malaria must be recognized precisely in order to treat the patient in time and to prevent further spread of infection. The standard diagnostic technique using microscopic examination is inefficient, the quality of the diagnosis depends on the quality of blood smears and experience of microscopists in classifying and counting infected and non-infected cells. Convolutional Neural Networks (CNN) is one of deep learning class that able to a…
MalariAI: A Label-Resilient Decoupled Framework for Annotation-Agnostic Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears
Malaria, a mosquito-borne disease caused by a parasite, kills over 1 million people globally each year. People, if left untreated, may develop severe complications, leading to death. Effective and accurate diagnosis is important for the management and control of malaria. Our research focuses on utilizing machine learning to improve the efficiency in Malaria diagnosis. We utilize a modified U-net architecture, as an unsupervised learning model, to conduct cell boundary detection. The blood cells infected by malaria are then identified in chromatic space by a Mahalanobis distance algorithm. Both…
University College London（伦敦大学学院）；University of Ibadan（伊巴丹大学）；University College Hospital, Ibadan（伊巴丹大学学院医院）
Fast accurate diagnosis of malaria is still a global health challenge for which automated digital-pathology approaches could provide scalable solutions amenable to be deployed in low-to-middle income countries. Here we address the problem of Extended Depth-of-Field (EDoF) in thick blood film microscopy for rapid automated malaria diagnosis. High magnification oil-objectives (100x) with large numerical aperture are usually preferred to resolve the fine structural details that help separate true parasites from distractors. However, such objectives have a very limited depth-of-field requiring the…
