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 samples as cancerous or normal samples by taking a shot of blood smear and passing it as an input to the system that will check whether it contains cancer or not. In case of containing cancer cells, then the hematological expert passes the sample to a more complex device such as flow cytometry to generate complete information about the progress of cancer in the blood.
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 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）
Center for Public Health Genomics（公共卫生基因组学中心）；Department of Biomedical Engineering University of Virginia（弗吉尼亚大学生物医学工程系）；University of Virginia（弗吉尼亚大学）
White Blood Cell (WBC) Leukaemia is detected through image-based classification. Convolutional Neural Networks are used to learn the features needed to classify images of cells a malignant or healthy. However, this type of model requires learning a large number of parameters and is difficult to interpret and explain. Explainable AI (XAI) attempts to alleviate this issue by providing insights to how models make decisions. Therefore, we present an XAI model which uses only 24 explainable and interpretable features and is highly competitive to other approaches by outperforming them by about 4.38\…
Deep Learning Algorithms for Early Diagnosis of Acute Lymphoblastic Leukemia
The American College of Greece（希腊美国学院）；National & Kapodistrean University of Athens（雅典国立卡波季斯特里安大学）；Biomedical Research Foundation, Academy of Athens（雅典科学院生物医学研究基金会）
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…
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…
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Tsinghua-berkeley Shenzhen Institute（清华-伯克利深圳学院）；The First Affiliated Hospital of Harbin Medical University（哈尔滨医科大学附属第一医院）；Food Inspection & Quarantine Center, Shenzhen Custom（深圳海关食品检验检疫中心）；Harbin Insitute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Shandong University（山东大学）
Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treat and cure acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification …
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…
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）
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 …
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…
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%).
MalariAI: A Label-Resilient Decoupled Framework for Annotation-Agnostic Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears
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,…
Khulna University of Engineering & Technology（库尔纳工程技术大学）；Bangabandhu Sheikh Mujibur Rahman Science & Technology University（班加班杜·谢赫·穆吉布·拉赫曼科技大学）；Khulna University（库尔纳大学）
Acute Lymphoblastic Leukemia (ALL) is a blood cell cancer characterized by numerous immature lymphocytes. Even though automation in ALL prognosis is an essential aspect of cancer diagnosis, it is challenging due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy demands experienced pathologists to carefully read the cell images, which is arduous, time-consuming, and often suffers inter-observer variations. This article has automated the ALL detection task from microscopic cell images, employing deep Convolutional Neural Networks (CN…
Sickle cell disease (SCD) is a major public health priority throughout much of the world, affecting millions of people. In many regions, particularly those in resource-limited settings, SCD is not consistently diagnosed. In Africa, where the majority of SCD patients reside, more than 50% of the 0.2-0.3 million children born with SCD each year will die from it; many of these deaths are in fact preventable with correct diagnosis and treatment. Here we present a deep learning framework which can perform automatic screening of sickle cells in blood smears using a smartphone microscope. This framew…
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…
Technical University of Munich（慕尼黑工业大学）；Siemens AG（西门子股份公司）
Diagnosing diseases such as leukemia or anemia requires reliable counts of blood cells. Hematologists usually label and count microscopy images of blood cells manually. In many cases, however, cells in different maturity states are difficult to distinguish, and in combination with image noise and subjectivity, humans are prone to make labeling mistakes. This results in labels that are often not reproducible, which can directly affect the diagnoses. We introduce TIMELY, a probabilistic model that combines pseudotime inference methods with inhomogeneous hidden Markov trees, which addresses this …
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））
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）
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…
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）
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 …
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…
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）
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…
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…
Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data
Koita Centre for Digital Health, Indian Institute of Technology Bombay（印度理工学院孟买分校科伊塔数字健康中心）；Zoho Corporation（卓豪公司）；Department of Chemical Engineering, Indian Institute of Technology Bombay（印度理工学院孟买分校化学工程系）；Sensing and Monitoring Foundation（传感与监测基金会）
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…
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）
This paper presents a method that improve state-of-the-art of the concave point detection methods as a first step to segment overlapping objects on images. It is based on the analysis of the curvature of the objects contour. The method has three main steps. First, we pre-process the original image to obtain the value of the curvature on each contour point. Second, we select regions with higher curvature and we apply a recursive algorithm to refine the previous selected regions. Finally, we obtain a concave point from each region based on the analysis of the relative position of their neighbour…
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）
