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 from different receptive areas. Next, the raw images are resampled to various resolutions to introduce variations in the receptive fields that are used for independently optimizing different forms of DilationNet scaled for different resolutions of images. Afterward, a feature fusion scheme is introduced with the proposed DeepFusionNet architecture for jointly optimizing the feature space of these individually trained networks operating on different levels of observations. All the convolutional layers of various forms of DilationNets that are optimized to extract spatial features from different resolutions of images are directly transferred to provide a variegated feature space from any image. Later, joint optimization of these spatial features is carried out in the DeepFusionNet to extract the most relevant representation of the sample image. This scheme offers the opportunity to explore the feature space extensively by varying the observation level to accurately diagnose the abnormality. Intense experimentations on a publicly available dataset show outstanding performance with accuracy over 99.5% outperforming other state-of-the-art approaches.
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…
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%).
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,…
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) whic…
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 …
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）
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…
MalariAI: A Label-Resilient Decoupled Framework for Annotation-Agnostic Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears
University of Ottawa（渥太华大学）；Minai University（米尼亚大学）
In this paper we discuss a new method for detecting leukemia in microscopic blood smear images using deep neural networks to diagnose leukemia early in blood. leukemia is considered one of the most dangerous mortality causes for a human being, the traditional process of diagnosis of leukemia in blood is complex, costly, and time-consuming, so patients could not receive medical treatment on time; Computer vision classification technique using deep learning can overcome the problems of traditional analysis of blood smears, our system for leukemia detection provides 97.3 % accuracy in classifying…
The Maharaja Sayajirao University of Baroda（巴罗达大君萨亚吉拉奥大学）
Malaria is one of the most common diseases caused by mosquitoes and is a great public health problem worldwide. Currently, for malaria diagnosis the standard technique is microscopic examination of a stained blood film. We propose use of Artificial Neural Networks (ANN) for the diagnosis of the disease in the red blood cell. For this purpose features / parameters are computed from the data obtained by the digital holographic images of the blood cells and is given as input to ANN which classifies the cell as the infected one or otherwise.
Malaria is usually diagnosed by a microbiologist by examining a small sample of blood smear. Reducing mortality from malaria infection is possible if it is diagnosed early and followed with appropriate treatment. While the WHO has set audacious goals of reducing malaria incidence and mortality rates by 90% in 2030 and eliminating malaria in 35 countries by that time, it still remains a difficult challenge. Computer-assisted diagnostics are on the rise these days as they can be used effectively as a primary test in the absence of or providing assistance to a physician or pathologist. The purpos…
Indian Institute of Technology, Bhilai（印度理工学院比莱分校）
Malaria is a disease of global concern according to the World Health Organization. Billions of people in the world are at risk of Malaria today. Microscopy is considered the gold standard for Malaria diagnosis. Microscopic assessment of blood samples requires the need of trained professionals who at times are not available in rural areas where Malaria is a problem. Full automation of Malaria diagnosis is a challenging task. In this work, we put forward a framework for diagnosis of malaria. We adopt a two layer approach, where we detect infected cells using a Faster-RCNN in the first layer, cro…
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…
RCC Institute of Information Technology（RCC信息技术学院）；Jadavpur University（贾达普大学）
The analysis and counting of blood cells in a microscope image can provide useful information concerning to the health of a person. In particular, morphological analysis of red blood cells deformations can effectively detect important disease like malaria. Blood images, obtained by the microscope, which is coupled with a digital camera, are analyzed by the computer for diagnosis or can be transmitted easily to clinical centers than liquid blood samples. Automatic analysis system for the presence of Plasmodium in microscopic image of blood can greatly help pathologists and doctors that typicall…
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 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…
Government College of Engineering & Ceramic Technology（政府工程与陶瓷技术学院）；Indian Institute of Science Education and Research (IISER)（印度科学教育与研究学院）
The advent of Deep Learning models like VGG-16 and Resnet-50 has considerably revolutionized the field of image classification, and by using these Convolutional Neural Networks (CNN) architectures, one can get a high classification accuracy on a wide variety of image datasets. However, these Deep Learning models have a very high computational complexity and so incur a high computational cost of running these algorithms as well as make it hard to interpret the results. In this paper, we present a shallow CNN architecture which gives the same classification accuracy as the VGG-16 and Resnet-50 m…
Thapar Institute of Engineering and Technology（塔帕尔工程技术学院）
Malaria is a life-threatening mosquito-borne blood disease, hence early detection is very crucial for health. The conventional method for the detection is a microscopic examination of Giemsa-stained blood smears, which needs a highly trained skilled technician. Automated classifications of different stages of malaria still a challenging task, especially having poor sensitivity in detecting the early trophozoite and late trophozoite or schizont stage with limited labelled datasize. The study aims to develop a fast, robust and fully automated system for the classification of different stages of …
Towards Field-Ready AI-based Malaria Diagnosis: A Continual Learning Approach
institutetext: MyC, Paris, France（MyC）；institutetext: CHU Campus, Ministère de la Santé et de l’hygiène Publique, Lomé, Togo（CHU Campus）；institutetext: Service de Parasitologie-Mycologie, Hôpital de La Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris (AP-HP), France（Service de Parasitologie-Mycologie）；institutetext: CHU of Montpellier, Université de Montpellier, Montpellier, France（CHU of Montpellier）；institutetext: PNLP Togo, Lomé, Togo（PNLP Togo）；institutetext: CHU Sylvanus Olympio, Lomé, Togo（CHU Sylvanus Olympio）；institutetext: Institut Pierre-Louis d’Epidémiologie et de Santé Publique, INSERM, Sorbonne Université, Paris, France（Institut Pierre-Louis d’Epidémiologie et de Santé Publique）；institutetext: Faculté des Sciences de la Santé, Université de Lomé, Lomé, Togo（Faculté des Sciences de la Santé）
MORPHA: Morphology-Constrained Training and the Limits of Cross-Acquisition Transfer in Low-Resource Malaria Microscopy
University of Nigeria, Nsukka（尼日利亚大学恩苏卡分校）；National Hospital Abuja（阿布贾国立医院）；State University of Medical and Applied Sciences, Igbo-Eno（伊博埃诺州立医学与应用科学大学）；Veritas University, Abuja（真理大学阿布贾校区）；Nnamdi Azikiwe University Teaching Hospital, Nnewi（纳姆迪·阿齐基韦大学教学医院）
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…
An estimated 300,000 new cases of leukemia are diagnosed each year which is 2.8 percent of all new cancer cases and the prevalence is rising day by day. The most dangerous and deadly type of leukemia is acute lymphoblastic leukemia (ALL), which affects people of all age groups, including children and adults. In this study, we propose an automated system to detect various-shaped ALL blast cells from microscopic blood smears images using Deep Neural Networks (DNN). The system can detect multiple subtypes of ALL cells with an accuracy of 98 percent. Moreover, we have developed a telediagnosis sof…
A COCO-Formatted Instance-Level Dataset for Plasmodium Falciparum Detection in Giemsa-Stained Blood Smears
MIRA Vision Microscopy GmbH（MIRA视觉显微镜 GmbH）；Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg（模式识别实验室，计算机科学系，弗赖堡-艾尔兰根-纽伦堡大学）
School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；The Hang Seng University of Hong Kong（香港恒生大学）
This paper proposes a novel automatic classification framework for the recognition of five types of white blood cells. Segmenting complete white blood cells from blood smears images and extracting advantageous features from them remain challenging tasks in the classification of white blood cells. Therefore, we present an adaptive threshold segmentation method to deal with blood smears images with non-uniform color and uneven illumination, which is designed based on color space information and threshold segmentation. Subsequently, after successfully separating the white blood cell from the bloo…
Massachusetts Institute of Technology（麻省理工学院）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Universidade de São Paulo（圣保罗大学）
Deep learning based models have had great success in object detection, but the state of the art models have not yet been widely applied to biological image data. We apply for the first time an object detection model previously used on natural images to identify cells and recognize their stages in brightfield microscopy images of malaria-infected blood. Many micro-organisms like malaria parasites are still studied by expert manual inspection and hand counting. This type of object detection task is challenging due to factors like variations in cell shape, density, and color, and uncertainty of s…
Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques
Addis Ababa Institute of Technology, Addis Ababa University（亚的斯亚贝巴大学亚的斯亚贝巴理工学院）；Opus College of Engineering, Marquette University（马凯特大学奥普斯工程学院）；Leuven Center for Affordable Healthcare Technology, KU Leuven（鲁汶大学鲁汶平价医疗技术中心）
Institute of AI for Health, Helmholtz Munich – German Research Center for Environmental Health（亥姆霍兹慕尼黑中心——德国环境健康研究中心健康人工智能研究所）；Politecnico Di Torino（都灵理工大学）；Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University Hospital Bonn（波恩大学医院）；Helmholtz AI, Helmholtz Munich – German Research Center for Environmental Health（亥姆霍兹慕尼黑中心——德国环境健康研究中心亥姆霍兹人工智能）
Diagnosing hematological malignancies requires identification and classification of white blood cells in peripheral blood smears. Domain shifts caused by different lab procedures, staining, illumination, and microscope settings hamper the re-usability of recently developed machine learning methods on data collected from different sites. Here, we propose a cross-domain adapted autoencoder to extract features in an unsupervised manner on three different datasets of single white blood cells scanned from peripheral blood smears. The autoencoder is based on an R-CNN architecture allowing it to focu…
In this paper, we describe how to apply image-to-image translation techniques to medical blood smear data to generate new data samples and meaningfully increase small datasets. Specifically, given the segmentation mask of the microscopy image, we are able to generate photorealistic images of blood cells which are further used alongside real data during the network training for segmentation and object detection tasks. This image data generation approach is based on conditional generative adversarial networks which have proven capabilities to high-quality image synthesis. In addition to synthesi…
EMFE: A lightweight, explainable machine learning framework for malaria cell classification
Daffodil International University（水仙国际大学）；Peter Faber Business School（彼得·费伯商学院）；Australian Catholic University（澳大利亚天主教大学）；Alliance University（联盟大学）；University of Alberta（阿尔伯塔大学）
MorphXAI: An Explainable Framework for Morphological Analysis of Parasites in Blood Smear Images
Department of Computer Science and Engineering, University of Texas at Arlington（德克萨斯大学阿灵顿分校计算机科学与工程系）；Department of Medicine, Division of Infectious Diseases, NYU Grossman School of Medicine（纽约大学格罗斯曼医学院医学系感染病科）
A Hybrid Feature Fusion Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Sample Using Gated Recurrent Unit and Uncertainty Quantification
Jagannath University（贾格纳特大学）；Green University of Bangladesh（孟加拉国绿色大学）
The automatic detection of White Blood Cells (WBC) still remains as an unsolved issue in medical imaging. The analysis of WBC images has engaged researchers from fields of medicine and computer vision alike. Since WBC can be approximated by an ellipsoid form, an ellipse detector algorithm may be successfully applied in order to recognize them. This paper presents an algorithm for the automatic detection of WBC embedded into complicated and cluttered smear images that considers the complete process as a multi-ellipse detection problem. The approach, based on the Differential Evolution (DE) algo…
Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images
Institute of AI for Health, Helmholtz Zentrum München – German Research Center for Environmental Health（慕尼黑亥姆霍兹中心人工智能健康研究所（德国环境健康研究中心））；Faculty of Mathematics, Technical University Munich（慕尼黑工业大学数学学院）；Computer Aided Medical Procedures, Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序研究所）；Computer Aided Medical Procedures, Johns Hopkins University（约翰霍普金斯大学计算机辅助医疗程序研究所）；Helmholtz AI, Helmholtz Zentrum München - German Research Center for Environmental Health（亥姆霍兹人工智能中心（慕尼黑亥姆霍兹中心，德国环境健康研究中心））
Indian Institute of Information Technology Allahabad（印度信息技术学院阿拉哈巴德）；Universitas Budi Luhur（布迪鲁胡尔大学）
Machine learning has endless applications in the health care industry. White blood cell classification is one of the interesting and promising area of research. The classification of the white blood cells plays an important part in the medical diagnosis. In practise white blood cell classification is performed by the haematologist by taking a small smear of blood and careful examination under the microscope. The current procedures to identify the white blood cell subtype is more time taking and error-prone. The computer aided detection and diagnosis of the white blood cells tend to avoid the h…
Pathologist-Like Explanations Unveiled: an Explainable Deep Learning System for White Blood Cell Classification
Indian Statistical Institute（印度统计研究所）；Drs Tribedi & Roy Diagnostic Laboratory（特里贝迪-罗伊医生诊断实验室）；Zoho Corporation（卓豪公司）；Centre for Digital Healthcare Technologies, MAKAUT（MAKAUT数字医疗技术中心）
UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices
Virginia Tech（弗吉尼亚理工大学）；BML Munjal University（BML Munjal大学）；University of Indiana Tech（印第安纳理工大学）；Nanjing Medical University（南京医科大学）
Neural Cellular Automata for Weakly Supervised Segmentation of White Blood Cells
Institute of AI for Health, Helmholtz Zentrum München – German Research Center for Environmental Health（人工智能与健康研究所，慕尼黑德国环境健康研究中心）；TUM School of Computation, Information and Technology, Technical University Munich（技术大学慕尼黑计算、信息与技术学院）；Institute of Chemical Epigenetics, Faculty of Chemistry and Pharmacy, Ludwig Maximilian University（化学表观遗传学研究所，路德维希-马克西米利安大学）；Computer Aided Medical Procedures, Technical University of Munich（医学辅助程序研究所，技术大学慕尼黑）
DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology
Helmholtz Munich（亥姆霍兹慕尼黑中心）；Ludwig-Maximilians-University Munich（慕尼黑大学）；Dr. von Haunersches Kinderspital（冯·豪内尔施儿童医院）；King’s College London（伦敦国王学院）；Technical University of Munich（慕尼黑工业大学）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）
BloodCell-Net: A lightweight convolutional neural network for the classification of all microscopic blood cell images of the human body
Khulna University of Engineering and Technology（库尔纳工程技术大学）；Bangladesh Atomic Energy Regulatory Authority（孟加拉国原子能监管局）；Zarqa University（扎尔卡大学）；Northumbria University（诺森比亚大学）；Bangabandhu Sheikh Mujibur Rahman Science and Technology University（班加班杜谢赫穆吉布尔·拉赫曼科技大学）；Northern Border University（北方边境大学）
Classification of White Blood Cells Using Machine and Deep Learning Models: A Systematic Review
University of Lahore（拉合尔大学）；University of Limerick（利默里克大学）；Dublin Institute of Technology（都柏林理工学院）；National University of Sciences and Technology（国家科技大学）
Point of care diagnostics using microscopy and computer vision methods have been applied to a number of practical problems, and are particularly relevant to low-income, high disease burden areas. However, this is subject to the limitations in sensitivity and specificity of the computer vision methods used. In general, deep learning has recently revolutionised the field of computer vision, in some cases surpassing human performance for other object recognition tasks. In this paper, we evaluate the performance of deep convolutional neural networks on three different microscopy tasks: diagnosis o…
Institute of AI for Health, Helmholtz Munich – German Research Center for Environmental Health（亥姆霍兹慕尼黑中心——德国环境健康研究中心健康人工智能研究所）；Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Explainability is a key requirement for computer-aided diagnosis systems in clinical decision-making. Multiple instance learning with attention pooling provides instance-level explainability, however for many clinical applications a deeper, pixel-level explanation is desirable, but missing so far. In this work, we investigate the use of four attribution methods to explain a multiple instance learning models: GradCAM, Layer-Wise Relevance Propagation (LRP), Information Bottleneck Attribution (IBA), and InputIBA. With this collection of methods, we can derive pixel-level explanations on for the …
This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morphological features of interest is now a crucial component of both biomedical research and disease diagnosis. While convolutional neural networks (CNNs) have dramatically improved the accuracy of counting cells and sub-cellular features from acquired digital image data, the overall throughput is still typically hindered by the limited space-bandwidth pro…
University of the Balearic Islands（巴利阿里群岛大学）；Health Research Institute of the Balearic Islands (IdISBa)（巴利阿里群岛健康研究所）
In this work we propose an approach to select the classification method and features, based on the state-of-the-art, with best performance for diagnostic support through peripheral blood smear images of red blood cells. In our case we used samples of patients with sickle-cell disease which can be generalized for other study cases. To trust the behavior of the proposed system, we also analyzed the interpretability. We pre-processed and segmented microscopic images, to ensure high feature quality. We applied the methods used in the literature to extract the features from blood cells and the mach…
Diagnosis Support of Sickle Cell Anemia by Classifying Red Blood Cell Shape in Peripheral Blood Images
Facultad de Ciencias Naturales y Exactas, Universidad de Oriente（自然科学与精确科学系，奥里ente大学）；Balearic Islands Health Research Institute (IdISBa)（巴利阿里群岛健康研究 institutes（IdISBa））；Department of Mathematics and Computer Science, Universitat de les Illes Balears（数学与计算机科学系，巴利阿里群岛大学）；Research Institute of Health Sciences (IUNICS)（健康科学研究院（IUNICS））；Computational biology and bioinformatics (BIOCOM) Research Group（计算生物学和生物信息学（BIOCOM）研究组）
