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Cheng W, Liu J, Wang C, Jiang R, Jiang M, Kong F. Application of image recognition technology in pathological diagnosis of blood smears. Clin Exp Med 2024; 24:181. [PMID: 39105953 PMCID: PMC11303489 DOI: 10.1007/s10238-024-01379-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Accepted: 05/13/2024] [Indexed: 08/07/2024]
Abstract
Traditional manual blood smear diagnosis methods are time-consuming and prone to errors, often relying heavily on the experience of clinical laboratory analysts for accuracy. As breakthroughs in key technologies such as neural networks and deep learning continue to drive digital transformation in the medical field, image recognition technology is increasingly being leveraged to enhance existing medical processes. In recent years, advancements in computer technology have led to improved efficiency in the identification of blood cells in blood smears through the use of image recognition technology. This paper provides a comprehensive summary of the methods and steps involved in utilizing image recognition algorithms for diagnosing diseases in blood smears, with a focus on malaria and leukemia. Furthermore, it offers a forward-looking research direction for the development of a comprehensive blood cell pathological detection system.
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Affiliation(s)
- Wangxinjun Cheng
- Center of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China
- Queen Mary College, Nanchang University, Nanchang, 330006, China
| | - Jingshuang Liu
- Center of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China
- Queen Mary College, Nanchang University, Nanchang, 330006, China
| | - Chaofeng Wang
- Center of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China
- Queen Mary College, Nanchang University, Nanchang, 330006, China
| | - Ruiyin Jiang
- Queen Mary College, Nanchang University, Nanchang, 330006, China
| | - Mei Jiang
- Department of Clinical Laboratory, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
| | - Fancong Kong
- Center of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
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MoradiAmin M, Yousefpour M, Samadzadehaghdam N, Ghahari L, Ghorbani M, Mafi M. Automatic classification of acute lymphoblastic leukemia cells and lymphocyte subtypes based on a novel convolutional neural network. Microsc Res Tech 2024; 87:1615-1626. [PMID: 38445461 DOI: 10.1002/jemt.24551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/14/2024] [Accepted: 02/26/2024] [Indexed: 03/07/2024]
Abstract
Acute lymphoblastic leukemia (ALL) is a life-threatening disease that commonly affects children and is classified into three subtypes: L1, L2, and L3. Traditionally, ALL is diagnosed through morphological analysis, involving the examination of blood and bone marrow smears by pathologists. However, this manual process is time-consuming, laborious, and prone to errors. Moreover, the significant morphological similarity between ALL and various lymphocyte subtypes, such as normal, atypic, and reactive lymphocytes, further complicates the feature extraction and detection process. The aim of this study is to develop an accurate and efficient automatic system to distinguish ALL cells from these similar lymphocyte subtypes without the need for direct feature extraction. First, the contrast of microscopic images is enhanced using histogram equalization, which improves the visibility of important features. Next, a fuzzy C-means clustering algorithm is employed to segment cell nuclei, as they play a crucial role in ALL diagnosis. Finally, a novel convolutional neural network (CNN) with three convolutional layers is utilized to classify the segmented nuclei into six distinct classes. The CNN is trained on a labeled dataset, allowing it to learn the distinguishing features of each class. To evaluate the performance of the proposed model, quantitative metrics are employed, and a comparison is made with three well-known deep networks: VGG-16, DenseNet, and Xception. The results demonstrate that the proposed model outperforms these networks, achieving an approximate accuracy of 97%. Moreover, the model's performance surpasses that of other studies focused on 6-class classification in the context of ALL diagnosis. RESEARCH HIGHLIGHTS: Deep neural networks eliminate the requirement for feature extraction in ALL classification The proposed convolutional neural network achieves an impressive accuracy of approximately 97% in classifying six ALL and lymphocyte subtypes.
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Affiliation(s)
- Morteza MoradiAmin
- Department of Physiology, Faculty of Medicine, AJA University of Medical Sciences, Tehran, Iran
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mitra Yousefpour
- Department of Physiology, Faculty of Medicine, AJA University of Medical Sciences, Tehran, Iran
| | - Nasser Samadzadehaghdam
- Department of Biomedical Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Laya Ghahari
- Department of Anatomy, Faculty of Medicine, AJA University of Medical Sciences, Tehran, Iran
| | - Mahdi Ghorbani
- Department of Medical Laboratory Sciences, School of Allied Medical Sciences, AJA University of Medical Sciences, Tehran, Iran
- Medical Biotechnology Research Center, AJA University of Medical Sciences, Tehran, Iran
| | - Majid Mafi
- Mechanical Engineering Department, Iran University of Science and Technology, Tehran, Iran
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Patel H, Shah H, Patel G, Patel A. Hematologic cancer diagnosis and classification using machine and deep learning: State-of-the-art techniques and emerging research directives. Artif Intell Med 2024; 152:102883. [PMID: 38657439 DOI: 10.1016/j.artmed.2024.102883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 04/16/2024] [Accepted: 04/18/2024] [Indexed: 04/26/2024]
Abstract
Hematology is the study of diagnosis and treatment options for blood diseases, including cancer. Cancer is considered one of the deadliest diseases across all age categories. Diagnosing such a deadly disease at the initial stage is essential to cure the disease. Hematologists and pathologists rely on microscopic evaluation of blood or bone marrow smear images to diagnose blood-related ailments. The abundance of overlapping cells, cells of varying densities among platelets, non-illumination levels, and the amount of red and white blood cells make it more difficult to diagnose illness using blood cell images. Pathologists are required to put more effort into the traditional, time-consuming system. Nowadays, it becomes possible with machine learning and deep learning techniques, to automate the diagnostic processes, categorize microscopic blood cells, and improve the accuracy of the procedure and its speed as the models developed using these methods may guide an assisting tool. In this article, we have acquired, analyzed, scrutinized, and finally selected around 57 research papers from various machine learning and deep learning methodologies that have been employed in the diagnosis of leukemia and its classification over the past 20 years, which have been published between the years 2003 and 2023 by PubMed, IEEE, Science Direct, Google Scholar and other pertinent sources. Our primary emphasis is on evaluating the advantages and limitations of analogous research endeavors to provide a concise and valuable research directive that can be of significant utility to fellow researchers in the field.
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Affiliation(s)
- Hema Patel
- Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, Charotar University of Science and Technology, CHARUSAT, Campus, Changa, 388421 Anand, Gujarat, India.
| | - Himal Shah
- QURE Haematology Centre, Ahmedabad 380006, Gujarat, India
| | - Gayatri Patel
- Ramanbhai Patel College of Pharmacy, Charotar University of Science and Technology, CHARUSAT, Campus, Changa, 388421 Anand, Gujarat, India
| | - Atul Patel
- Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, Charotar University of Science and Technology, CHARUSAT, Campus, Changa, 388421 Anand, Gujarat, India
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Elrefaie RM, Mohamed MA, Marzouk EA, Ata MM. A robust classification of acute lymphocytic leukemia-based microscopic images with supervised Hilbert-Huang transform. Microsc Res Tech 2024; 87:191-204. [PMID: 37715495 DOI: 10.1002/jemt.24425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/19/2023] [Accepted: 09/06/2023] [Indexed: 09/17/2023]
Abstract
Acute lymphocytic leukemia (ALL) is a malignant condition characterized by the development of blast cells in the bone marrow and their quick dissemination into the bloodstream. It primarily affects children and individuals over the age of 60. Manual blood testing, which has been around for a long time, may be slow. The likelihood of recognizing ALL in its early stages was increased by automating the diagnosis. This research developed an improved criterion for classifying ALL microscopic images into two categories: normal images and blast images. First, to save processing time, innovative image preprocessing techniques were employed to gather data for data augmentation, enhancement, and conversion. The K-means clustering technique was also utilized to effectively segment the relevant nuclei from the background. Furthermore, the most salient features were extracted using an empirical mode decomposition (EMD) based on the Hilbert-Huang transform. MATLAB functions such as principal component analysis, gray level co-occurrence matrix, local binary pattern, shape features, discrete cosine transform, discrete Fourier transform, discrete wavelet transform, and independent component analysis have been used and compared with EMD. The Bayesian regularization (BR) method has been implemented in the neural networks (NNs) classifier. Along with NNs, other classifiers such as support vector machine, K-nearest neighbors, random forest, naive Bayes, logistic regression, and decision tree have been used, evaluated, and contrasted with NNs. According to experimental findings, the ALL-IDB2 (Image Database 2) dataset's NNs-based-EMD model classified objects with an accuracy of 98.7%, sensitivity of 99.3%, and specificity of 98.1%. RESEARCH HIGHLIGHTS: Implement a robust method for classifying normal and blast ALL images in the state of the art using the combination of the BR algorithm and the neural networks classifier. Perform robust data processing via data augmentation and conversion from RGB (Red, Green, and Blue) image LAB (Luminosity, A: color space, B: color space) image. Extract the nuclei correctly from the background image using k-means clustering. Extract the most salient features from the segmented images using EMD in the state of the art of HHT.
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Affiliation(s)
- Reem Magdy Elrefaie
- Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Mohamed A Mohamed
- Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Elsaid A Marzouk
- Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Mohamed Maher Ata
- School of Computational Sciences and Artificial Intelligence (CSAI), Zewail City of Science and Technology, 6th of October City, Giza, Egypt
- Department of Communications and Electronics Engineering, MISR Higher Institute for Engineering and Technology, Mansoura, Egypt
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Tarquino J, Arabyarmohammadi S, Tejada RE, Madabhushi A, Romero E. Intra-nucleus mosaic pattern (InMop) and whole-cell Haralick combined-descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images. Cytometry A 2023; 103:857-867. [PMID: 37565838 PMCID: PMC10841385 DOI: 10.1002/cyto.a.24785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 07/14/2023] [Accepted: 08/08/2023] [Indexed: 08/12/2023]
Abstract
Acute leukemia is usually diagnosed when a test of peripheral blood shows at least 20% of abnormal immature cells (blasts), a figure even lower in case of recurrent cytogenetic abnormalities. Blast identification is crucial for white blood cell (WBC) counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter- and intraobserver variability. The present work introduces an image combined-descriptor to detect blasts and determine their probable lineage. This strategy uses an intra-nucleus mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner-nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically-segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co-occurrence matrix representation. Both InMoP and Haralick-based descriptors are calculated using the b-channel from Lab color-space. The combined-descriptor is assessed by differentiating blasts from nonleukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database-D1 (n = 260) is composed of healthy and acute lymphoid leukemia (ALL) single cell images, and second database-D2 contains acute myeloid leukemia (AML) blasts (n = 3294) and nonblast (n = 15,071) cell images. In a first experiment, blasts versus nonblast differentiation is performed by training with a subset of D2 (n = 6588) and testing in D1 (n = 260), obtaining a training AUC of 0.991 ± 0.002 and AUC = 0.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state-of-the-art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from nonblast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1-score.
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Affiliation(s)
- Jonathan Tarquino
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Sara Arabyarmohammadi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Rafael Enrique Tejada
- Department of internal medicine, Hemato-oncology unit, Medicine Faculty, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Atlanta Veterans Medical Center, Atlanta, GA, USA
| | - Eduardo Romero
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia
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Yadav DP, Kumar D, Jalal AS, Kumar A, Singh KU, Shah MA. Morphological diagnosis of hematologic malignancy using feature fusion-based deep convolutional neural network. Sci Rep 2023; 13:16988. [PMID: 37813973 PMCID: PMC10562409 DOI: 10.1038/s41598-023-44210-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 10/05/2023] [Indexed: 10/11/2023] Open
Abstract
Leukemia is a cancer of white blood cells characterized by immature lymphocytes. Due to blood cancer, many people die every year. Hence, the early detection of these blast cells is necessary for avoiding blood cancer. A novel deep convolutional neural network (CNN) 3SNet that has depth-wise convolution blocks to reduce the computation costs has been developed to aid the diagnosis of leukemia cells. The proposed method includes three inputs to the deep CNN model. These inputs are grayscale and their corresponding histogram of gradient (HOG) and local binary pattern (LBP) images. The HOG image finds the local shape, and the LBP image describes the leukaemia cell's texture pattern. The suggested model was trained and tested with images from the AML-Cytomorphology_LMU dataset. The mean average precision (MAP) for the cell with less than 100 images in the dataset was 84%, whereas for cells with more than 100 images in the dataset was 93.83%. In addition, the ROC curve area for these cells is more than 98%. This confirmed proposed model could be an adjunct tool to provide a second opinion to a doctor.
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Affiliation(s)
- D P Yadav
- Department of Computer Engineering and Applications, G.L.A. University, Mathura, 281406, India
| | - Deepak Kumar
- Department of Computer Science, NIT Meghalaya, Shillong, 793003, India
| | - Anand Singh Jalal
- Department of Computer Engineering and Applications, G.L.A. University, Mathura, 281406, India
| | - Ankit Kumar
- Department of Computer Engineering and Applications, G.L.A. University, Mathura, 281406, India
| | - Kamred Udham Singh
- School of Computing, Graphic Era Hill University, Dehradun, 248002, India
| | - Mohd Asif Shah
- Kebri Dehar University, Kebri Dehar, Ethiopia.
- Woxsen University, Kamkole, Sadasivpet, Hyderabad, Telangana, 502345, India.
- Division of Research and Development, Lovely Professional University, Phagwara, Punjab, 144001, India.
- Research Fellow, INTI International University, Persiaran Perdana BBN Putra, Nilai, Negeri Sembilan, 71800, Malaysia.
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Kaur M, AlZubi AA, Jain A, Singh D, Yadav V, Alkhayyat A. DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images. Diagnostics (Basel) 2023; 13:2752. [PMID: 37685290 PMCID: PMC10486457 DOI: 10.3390/diagnostics13172752] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 09/10/2023] Open
Abstract
Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To address this challenge, researchers have turned to advanced technologies such as deep learning (DL) models. These models leverage the power of artificial intelligence to analyze complex patterns and features in medical images and data, enabling faster and more accurate diagnosis of ALL. However, the existing DL-based ALL diagnosis suffers from various challenges, such as computational complexity, sensitivity to hyperparameters, and difficulties with noisy or low-quality input images. To address these issues, in this paper, we propose a novel Deep Skip Connections-Based Dense Network (DSCNet) tailored for ALL diagnosis using peripheral blood smear images. The DSCNet architecture integrates skip connections, custom image filtering, Kullback-Leibler (KL) divergence loss, and dropout regularization to enhance its performance and generalization abilities. DSCNet leverages skip connections to address the vanishing gradient problem and capture long-range dependencies, while custom image filtering enhances relevant features in the input data. KL divergence loss serves as the optimization objective, enabling accurate predictions. Dropout regularization is employed to prevent overfitting during training, promoting robust feature representations. The experiments conducted on an augmented dataset for ALL highlight the effectiveness of DSCNet. The proposed DSCNet outperforms competing methods, showcasing significant enhancements in accuracy, sensitivity, specificity, F-score, and area under the curve (AUC), achieving increases of 1.25%, 1.32%, 1.12%, 1.24%, and 1.23%, respectively. The proposed approach demonstrates the potential of DSCNet as an effective tool for early and accurate ALL diagnosis, with potential applications in clinical settings to improve patient outcomes and advance leukemia detection research.
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Affiliation(s)
- Manjit Kaur
- School of Computer Science and Artificial Intelligence, SR University, Warangal 506371, India;
| | - Ahmad Ali AlZubi
- Department of Computer Science, Community College, King Saud University, Riyadh 11421, Saudi Arabia;
| | - Arpit Jain
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada 522302, India;
| | - Dilbag Singh
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Research and Development Cell, Lovely Professional University, Phagwara 144411, India
| | - Vaishali Yadav
- School of Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, India
| | - Ahmed Alkhayyat
- College of Technical Engineering, The Islamic University, Najaf 7003, Iraq;
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Ashrafi F, Sadeghi A, Derakhshandeh A, Oghab P. The feasibility of PETHEMA ALL-96 regimen on treatment of patients with acute lymphoid leukemia. JOURNAL OF RESEARCH IN MEDICAL SCIENCES : THE OFFICIAL JOURNAL OF ISFAHAN UNIVERSITY OF MEDICAL SCIENCES 2023; 28:30. [PMID: 37213449 PMCID: PMC10199372 DOI: 10.4103/jrms.jrms_4_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 10/19/2022] [Accepted: 11/14/2022] [Indexed: 05/23/2023]
Abstract
Background Asparaginase-based treatment regimen for acute lymphocytic leukemia (ALL) is considered as feasible, but there is still a lack of data. In this study, considering the results of other regimen that were not optimum in previous studies. Here, we aimed to investigate the feasibility of PETHEMA ALL-96 treatment regimen. Materials and Methods This is a retrospective feasibility study that was performed in 2019-2021 on 13 patients diagnosed with B-cell ALL. Patients were treated by PETHEMA ALL-96 regimen during induction, consolidation, reinduction, and maintenance phases. Patients were followed for 2 years after initiation of PETHEMA ALL-96 regimen for disease-free survival (DFS) and overall survival (OS) of all patients were evaluated after 2 years. Results Data of 11 patients were analyzed. Within 28 days after treatments, all patients (100%) had no blasts in the bone marrow that was considered as complete remission (CR). The CR rate was 100% within 6 months and 12 months and 81.8% within 2 years after the treatments. Evaluation of OS, CR, and DFS regarding 6, 12, and 24 months showed 100% for all items after 6 and 12 months. After 24 months, the CR was 90.9%, the OS was 81.8% and the DFS was 90.9%. None of the patients died during the induction phase and during the 12 months study. No side effects were observed. Conclusion The PETHEMA ALL-96 had high feasibility and survival rates with no side effects during the study course. It is believed that PETHEMA ALL-96 regimen has beneficial outcomes in young patients with ALL.
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Affiliation(s)
- Farzaneh Ashrafi
- Aquired Immunodeficiency Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Alireza Sadeghi
- Department of Hematology-Oncology, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Ali Derakhshandeh
- Department of Hematology-Oncology, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Padideh Oghab
- Department of Hematology-Oncology, Isfahan University of Medical Sciences, Isfahan, Iran
- Address for correspondence: Dr. Padideh Oghab, School of Medicine, Al-zahra Hospital, Isfahan University of Medical Sciences, Isfahan, Iran. E-mail:
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Alzoubi I, Bao G, Zheng Y, Wang X, Graeber MB. Artificial intelligence techniques for neuropathological diagnostics and research. Neuropathology 2022. [PMID: 36443935 DOI: 10.1111/neup.12880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/17/2022] [Accepted: 10/23/2022] [Indexed: 12/03/2022]
Abstract
Artificial intelligence (AI) research began in theoretical neurophysiology, and the resulting classical paper on the McCulloch-Pitts mathematical neuron was written in a psychiatry department almost 80 years ago. However, the application of AI in digital neuropathology is still in its infancy. Rapid progress is now being made, which prompted this article. Human brain diseases represent distinct system states that fall outside the normal spectrum. Many differ not only in functional but also in structural terms, and the morphology of abnormal nervous tissue forms the traditional basis of neuropathological disease classifications. However, only a few countries have the medical specialty of neuropathology, and, given the sheer number of newly developed histological tools that can be applied to the study of brain diseases, a tremendous shortage of qualified hands and eyes at the microscope is obvious. Similarly, in neuroanatomy, human observers no longer have the capacity to process the vast amounts of connectomics data. Therefore, it is reasonable to assume that advances in AI technology and, especially, whole-slide image (WSI) analysis will greatly aid neuropathological practice. In this paper, we discuss machine learning (ML) techniques that are important for understanding WSI analysis, such as traditional ML and deep learning, introduce a recently developed neuropathological AI termed PathoFusion, and present thoughts on some of the challenges that must be overcome before the full potential of AI in digital neuropathology can be realized.
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Affiliation(s)
- Islam Alzoubi
- School of Computer Science The University of Sydney Sydney New South Wales Australia
| | - Guoqing Bao
- School of Computer Science The University of Sydney Sydney New South Wales Australia
| | - Yuqi Zheng
- Ken Parker Brain Tumour Research Laboratories Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney Camperdown New South Wales Australia
| | - Xiuying Wang
- School of Computer Science The University of Sydney Sydney New South Wales Australia
| | - Manuel B. Graeber
- Ken Parker Brain Tumour Research Laboratories Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney Camperdown New South Wales Australia
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Saleem S, Amin J, Sharif M, Mallah GA, Kadry S, Gandomi AH. Leukemia segmentation and classification: A comprehensive survey. Comput Biol Med 2022; 150:106028. [PMID: 36126356 DOI: 10.1016/j.compbiomed.2022.106028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 07/11/2022] [Accepted: 08/20/2022] [Indexed: 11/30/2022]
Abstract
Blood is made up of leukocytes (WBCs), erythrocytes (RBCs), and thrombocytes. The ratio of blood cancer diseases is increasing rapidly, among which leukemia is one of the famous cancer which may lead to death. Leukemia cancer is initiated by the unnecessary growth of immature WBCs present in the sponge tissues of bone marrow. It is generally analyzed by etiologists by perceiving slides of blood smear images under a microscope. The morphological features and blood cells count facilitated the etiologists to detect leukemia. Due to the late detection and expensive instruments used for leukemia analysis, the death rate has risen significantly. The fluorescence-based cell sorting technique and manual recounts using a hemocytometer are error-prone and imprecise. Leukemia detection methods consist of pre-processing, segmentation, features extraction, and classification. In this article, recent deep learning methodologies and challenges for leukemia detection are discussed. These methods are helpful to examine the microscopic blood smears images and for the detection of leukemia more accurately.
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Affiliation(s)
- Saba Saleem
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Javaria Amin
- Department of Computer Science, University of Wah, Wah Cantt, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | | | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway; Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| | - Amir H Gandomi
- Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
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Sampathila N, Chadaga K, Goswami N, Chadaga RP, Pandya M, Prabhu S, Bairy MG, Katta SS, Bhat D, Upadya SP. Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images. Healthcare (Basel) 2022; 10:1812. [PMID: 36292259 PMCID: PMC9601337 DOI: 10.3390/healthcare10101812] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/07/2022] [Accepted: 09/09/2022] [Indexed: 08/27/2023] Open
Abstract
Acute lymphoblastic leukemia (ALL) is a rare type of blood cancer caused due to the overproduction of lymphocytes by the bone marrow in the human body. It is one of the common types of cancer in children, which has a fair chance of being cured. However, this may even occur in adults, and the chances of a cure are slim if diagnosed at a later stage. To aid in the early detection of this deadly disease, an intelligent method to screen the white blood cells is proposed in this study. The proposed intelligent deep learning algorithm uses the microscopic images of blood smears as the input data. This algorithm is implemented with a convolutional neural network (CNN) to predict the leukemic cells from the healthy blood cells. The custom ALLNET model was trained and tested using the microscopic images available as open-source data. The model training was carried out on Google Collaboratory using the Nvidia Tesla P-100 GPU method. Maximum accuracy of 95.54%, specificity of 95.81%, sensitivity of 95.91%, F1-score of 95.43%, and precision of 96% were obtained by this accurate classifier. The proposed technique may be used during the pre-screening to detect the leukemia cells during complete blood count (CBC) and peripheral blood tests.
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Affiliation(s)
- Niranjana Sampathila
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Krishnaraj Chadaga
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Neelankit Goswami
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Rajagopala P. Chadaga
- Department of Mechanical & Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Mayur Pandya
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Srikanth Prabhu
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Muralidhar G. Bairy
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Swathi S. Katta
- Manipal Institute of Management, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Devadas Bhat
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Sudhakara P. Upadya
- Manipal School of Information Science, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
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Detecting Malignant Leukemia Cells Using Microscopic Blood Smear Images: A Deep Learning Approach. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136317] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Leukemia is a form of blood cancer that develops when the human body’s bone marrow contains too many white blood cells. This medical condition affects adults and is considered a prevalent form of cancer in children. Treatment for leukaemia is determined by the type and the extent to which cancer has developed across the body. It is crucial to diagnose leukaemia early in order to provide adequate care and to cure patients. Researchers have been working on advanced diagnostics systems based on Machine Learning (ML) approaches to diagnose leukaemia early. In this research, we employ deep learning (DL) based convolutional neural network (CNN) and hybridized two individual blocks of CNN named CNN-1 and CNN-2 to detect acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML), and multiple myeloma (MM). The proposed model detects malignant leukaemia cells using microscopic blood smear images. We construct a dataset of about 4150 images from a public directory. The main challenges were background removal, ripping out un-essential blood components of blood supplies, reduce the noise and blurriness and minimal method for image segmentation. To accomplish the pre-processing and segmentation, we transform RGB color-space into the greyscale 8-bit mode, enhancing the contrast of images using the image intensity adjustment method and adaptive histogram equalisation (AHE) method. We increase the structure and sharpness of images by multiplication of binary image with the output of enhanced images. In the next step, complement is done to get the background in black colour and nucleus of blood in white colour. Thereafter, we applied area operation and closing operation to remove background noise. Finally, we multiply the final output to source image to regenerate the images dataset in RGB colour space, and we resize dataset images to [400, 400]. After applying all methods and techniques, we have managed to get noiseless, non-blurred, sharped and segmented images of the lesion. In next step, enhanced segmented images are given as input to CNNs. Two parallel CCN models are trained, which extract deep features. The extracted features are further combined using the Canonical Correlation Analysis (CCA) fusion method to get more prominent features. We used five classification algorithms, namely, SVM, Bagging ensemble, total boosts, RUSBoost, and fine KNN, to evaluate the performance of feature extraction algorithms. Among the classification algorithms, Bagging ensemble outperformed the other algorithms by achieving the highest accuracy of 97.04%.
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13
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Liu K, Hu J. Classification of acute myeloid leukemia M1 and M2 subtypes using machine learning. Comput Biol Med 2022; 147:105741. [PMID: 35738057 DOI: 10.1016/j.compbiomed.2022.105741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 05/24/2022] [Accepted: 06/11/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Classification of acute myeloid leukemia (AML) relies on manual analysis of bone marrow or peripheral blood smear images. We aimed to construct a machine learning model for automatic classification of AML-M1 and M2 subtypes in bone marrow smear images. METHODS Bone marrow smear images of AML patients were extracted from the Cancer Imaging Archive (TCIA) open database. Classification criteria of AML subtypes were based on the French-American-British (FAB) classification system. Random forest method and broad learning system (BLS) were used to develop the classification model. Morphological features, radiomics features, and clinical features were extracted. The performance of the classification model was evaluated by calculating accuracy, precision, recall, F1-score, and area under the curve (AUC). A total of 50 bone marrow smear images (AML-M1, 31 cases; AML-M2, 19 cases) with 500 slices were included in this study. RESULTS A total of 43 morphological features, 276 radiomics features, and 1 clinical feature were extracted. Finally, 9 variables including 2 morphological features, 6 radiomics features, and 1 clinical feature were selected into the classification model. The best classification performance was observed in the random forest model with 9 variables, with the average accuracy, AUC, F1-score, recall, and precision of the model being 0.998 ± 0.003, 0.998 ± 0.004, 0.998 ± 0.004, 0.996 ± 0.009, and 1 ± 0, respectively. CONCLUSION The random forest model performed well for the classification of AML-M1 and M2, which may provide a tool for clinicians to classify AML-M1 and M2.
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Affiliation(s)
- Ke Liu
- Department of Hematology, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, 471003, China.
| | - Jie Hu
- Department of Medical Record Management, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, 471003, China
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14
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Haider A, Arsalan M, Lee YW, Park KR. Deep features aggregation-based joint segmentation of cytoplasm and nuclei in white blood cells. IEEE J Biomed Health Inform 2022; 26:3685-3696. [PMID: 35635825 DOI: 10.1109/jbhi.2022.3178765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
White blood cells (WBCs), also known as leukocytes, are one of the valuable parts of the blood and immune system. Typically, pathologists use microscope for the manual inspection of blood smears which is a time-consuming, error-prone, and labor-intensive procedure. To address these issues, we present two novel shallow networks: a leukocyte deep segmentation network (LDS-Net) and leukocyte deep aggregation segmentation network (LDAS-Net) for the joint segmentation of cytoplasm and nuclei in WBC images. LDS-Net is a shallow architecture with three downsampling stages and seven convolution layers. LDAS-Net is an extended version of LDS-Net that utilizes a novel pool-less low-level information transfer bridge to transfer low-level information to the deep layers of the network. This information is aggregated with deep features in a dense feature concatenation block to achieve accurate cytoplasm and nuclei joint segmentation. We evaluated our developed architectures on four WBC publicly available datasets. For cytoplasmic segmentation in WBCs, the proposed method achieved the dice coefficients of 98.97%, 99.0%, 96.05%, and 98.79% on Datasets 1, 2, 3, and 4, respectively. For nuclei segmentation, the dice coefficients of 96.35% and 98.09% are achieved for Datasets 1 and 2, respectively. Proposed method outperforms state-of-the-art methods with superior computational efficiency and requires only 6.5 million trainable parameters.
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Kumar I, Bhatt C, Vimal V, Qamar S. Automated white corpuscles nucleus segmentation using deep neural network from microscopic blood smear. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-189773] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The white corpuscles nucleus segmentation from microscopic blood images is major steps to diagnose blood-related diseases. The perfect and speedy segmentation system assists the hematologists to identify the diseases and take appropriate decision for better treatment. Therefore, fully automated white corpuscles nucleus segmentation model using deep convolution neural network, is proposed in the present study. The proposed model uses the combination of ‘binary_cross_entropy’ and ‘adam’ for maintaining learning rate in each network weight. To validate the potential and capability of the above proposed solution, ALL-IDB2 dataset is used. The complete set of images is partitioned into training and testing set and tedious experimentations have been performed. The best performing model is selected and the obtained training and testing accuracy of best performing model is reported as 98.69 % and 99.02 %, respectively. The staging analysis of proposed model is evaluated using sensitivity, specificity, Jaccard index, dice coefficient, accuracy and structure similarity index. The capability of proposed model is compared with performance of the region-based contour and fuzzy-based level-set method for same set of images and concluded that proposed model method is more accurate and effective for clinical purpose.
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Affiliation(s)
- Indrajeet Kumar
- Graphic Era Hill University, CSE Department, Dehradun, India
| | | | - Vrince Vimal
- Graphic Era Hill University, CSE Department, Dehradun, India
| | - Shamimul Qamar
- College of Science and Arts Dhahran Al Janub King Khalid University ABHA, Saudi Arabia
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16
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Arivuselvam B, Sudha S. Leukemia classification using the deep learning method of CNN. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:567-585. [PMID: 35253723 DOI: 10.3233/xst-211055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
BACKGROUND Processing Low-Intensity Medical Images (LI-MI) is difficult as outcomes are varied when it comes to manual examination, which is also a time-consuming process. OBJECTIVE To improve the quality of low-intensity images and identify the leukemia classification by utilizing CNN-based Deep Learning (DCNN) strategy. METHODS The strategies employed for the recognition of leukemia classifications in the advised strategy are DCNN (ResNet-34 & DenseNet-121). The histogram equalization-based adaptive gamma correction followed by guided filtering applies to study the improvement in intensity and preserve the essential details of the image. The DCNN is used as a feature extractor to help classify leukemia types. Two datasets of ASH with 520 images and ALL-IDB with 559 images are used in this study. In 1,079 images, 779 are positive cases depicting leukemia and 300 images are negative (normal) cases. Thus, to validate performance of this DCNN strategy, ASH and ALL-IDB datasets are promoted in the investigation process to classify between positive and negative images. RESULTS The DCNN classifier yieldes the overall classification accuracy of 99.2% and 98.4%, respectively. In addition, the achieved classification specificity, sensitivity, and precision are 99.3%, 98.7%, 98.4%, and 98.9%, 98.4%,98.6% applying to two datasets, respectively, which are higher than the performance using other machine learning classifiers including support vector machine, decision tree, naive bayes, random forest and VGG-16. CONCLUSION Ths study demonstrates that the proposed DCNN enables to improve low-intensity images and accuracry of leukemia classification, which is superior to many of other machine leaning classifiers used in this research field.
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Affiliation(s)
- B Arivuselvam
- Department of Electronics and Communication Engineering, Easwari Engineering College, Chennai, India
| | - S Sudha
- Department of Electronics and Communication Engineering, Easwari Engineering College, Chennai, India
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Anilkumar KK, Manoj VJ, Sagi TM. Automated detection of leukemia by pretrained deep neural networks and transfer learning: A comparison. Med Eng Phys 2021; 98:8-19. [PMID: 34848042 DOI: 10.1016/j.medengphy.2021.10.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 09/04/2021] [Accepted: 10/11/2021] [Indexed: 10/20/2022]
Abstract
Leukemia is usually diagnosed by viewing the smears of blood and bone marrow using microscopes and complex Cytochemical tests can be used to authorize and classify leukemia. But these methods are costly, slow and affected by the proficiency and expertise of the specialists concerned. Leukemia can be detected with the help of image processing-based methods by analyzing microscopic smear images to detect the presence of leukemic cells and such techniques are simple, fast, cheap and not biased by the specialists. The proposed study presents a computer aided diagnosis system that uses pretrained deep Convolutional Neural Networks (CNNs) for detection of leukemia images against normal images. The use of pretrained networks is comparatively an easy method of applying deep learning for image analysis and the comparison results of the present study can be used to choose appropriate networks for diagnostic tasks. The microscopic images used in the proposed work were downloaded from a public dataset ALL-IDB. In the proposed work, image classification is done without using any image segregation and feature extraction practices and the study used pretrained series network AlexNet, VGG-16, VGG-19, Directed Acyclic Graph (DAG) networks GoogLeNet, Inceptionv3, MobileNet-v2, Xception, DenseNet-201, Inception-ResNet-v2 and residual networks ResNet-18, ResNet-50 and ResNet-101 for performing the classification and comparison. A classification accuracy of 100% is obtained with all the pretrained networks used in the study for ALL_IDB1 dataset and for ALL_IDB2 dataset, 100% accuracy is obtained with all networks except the AlexNet and VGG-16. The efficacy of three optimization algorithms Stochastic Gradient Descent with Momentum (SGDM), Root Mean Square propagation (RMSprop) and Adaptive Moment estimation (ADAM) is also compared in all the classifications performed. The study considered the detection of leukemia in general only, and classification of leukemia into different types can be attempted as a future work.
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Affiliation(s)
- K K Anilkumar
- Department of Electronics and Communication, Cochin University College of Engineering Kuttanad, Cochin University of Science and Technology, Pulincunnu P.O., Alappuzha, Kerala 688504, India.
| | - V J Manoj
- Department of Electronics and Communication, Cochin University College of Engineering Kuttanad, Cochin University of Science and Technology, Pulincunnu P.O., Alappuzha, Kerala 688504, India
| | - T M Sagi
- Department of Medical Lab Technology, St. Thomas College of Allied Health Sciences, Changanacherry P.O., Kottayam, Kerala 686104, India
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18
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Huang B, Wei Z, Tang X, Fujita H, Cai Q, Gao Y, Wu T, Zhou L. Deep learning network for medical volume data segmentation based on multi axial plane fusion. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 212:106480. [PMID: 34736168 DOI: 10.1016/j.cmpb.2021.106480] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 10/13/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE High-dimensional data generally contains more accurate information for medical image, e.g., computerized tomography (CT) data can depict the three dimensional structure of organs more precisely. However, the data in high-dimension often needs enormous computation and has high memory requirements in the deep learning convolution networks, while dimensional reduction usually leads to performance degradation. METHODS In this paper, a two-dimensional deep learning segmentation network was proposed for medical volume data based on multi-pinacoidal plane fusion to cover more information under the control of computation.This approach has conducive compatibility while using the model proposed to extract the global information between different inputs layers. RESULTS Our approach has worked in different backbone network. Using the approach, DeepUnet's Dice coefficient (Dice) and Positive Predictive Value (PPV) are 0.883 and 0.982 showing the satisfied progress. Various backbones can enjoy the profit of the method. CONCLUSIONS Through the comparison of different backbones, it can be found that the proposed network with multi-pinacoidal plane fusion can achieve better results both quantitively and qualitatively.
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Affiliation(s)
- Bo Huang
- Shanghai University of Engineering Science, 333 Longteng Road, Songjiang District, Shanghai, Shanghai, 201620, China.
| | - Ziran Wei
- Shanghai Changzheng Hospital, 415 Fengyang Road, Huangpu District, Shanghai, Shanghai, 200003, China
| | - Xianhua Tang
- Changzhou United Imaging Healthcare Surgical Technology Co.,Ltd, No.5 Longfan Road, Wujin High-Tech Industrial Development Zone, Changzhou, China
| | - Hamido Fujita
- Faculty of Information Technology, Ho Chi Minh City University of Technology(HUTECH), Ho Chi Minh City, Vietnam; i-SOMET.org Incorporated Association, Iwate 020-0104, Japan; Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI), University of Granada, Granada, Spain; College of Mathematical Sciences, Harbin Engineering University, Harbin 150001, China.
| | - Qingping Cai
- Shanghai Changzheng Hospital, 415 Fengyang Road, Huangpu District, Shanghai, Shanghai, 200003, China
| | - Yongbin Gao
- Shanghai University of Engineering Science, 333 Longteng Road, Songjiang District, Shanghai, Shanghai, 201620, China
| | - Tao Wu
- Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Liang Zhou
- Shanghai University of Medicine & Health Sciences, Shanghai, China
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Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00564-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
AbstractWhite blood cells, WBCs for short, are an essential component of the human immune system. These cells are our body's first line of defense against infections and diseases caused by bacteria, viruses, and fungi, as well as abnormal and external substances that may enter the bloodstream. A wrong WBC count can signify dangerous viral infections, autoimmune disorders, cancer, sarcoidosis, aplastic anemia, leukemia, tuberculosis, etc. A lot of these diseases and disorders can be extremely painful and often result in death. Leukemia is among the more common types of blood cancer and when left undetected leads to death. An early diagnosis is necessary which is possible by looking at the shapes and determining the numbers of young and immature WBCs to see if they are normal or not. Performing this task manually is a cumbersome, expensive, and time-consuming process for hematologists, and therefore computer-aided systems have been developed to help with this problem. This paper proposes an improved method of classification of WBCs utilizing a combination of preprocessing, convolutional neural networks (CNNs), feature selection algorithms, and classifiers. In preprocessing, contrast-limited adaptive histogram equalization (CLAHE) is applied to the input images. A CNN is designed and trained to be used for feature extraction along with ResNet50 and EfficientNetB0 networks. Ant colony optimization is used to select the best features which are then serially fused and passed onto classifiers such as support vector machine (SVM) and quadratic discriminant analysis (QDA) for classification. The classification accuracy achieved on the Blood Cell Images dataset is 98.44%, which shows the robustness of the proposed work.
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Liu Z, Jin L, Chen J, Fang Q, Ablameyko S, Yin Z, Xu Y. A survey on applications of deep learning in microscopy image analysis. Comput Biol Med 2021; 134:104523. [PMID: 34091383 DOI: 10.1016/j.compbiomed.2021.104523] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/13/2021] [Accepted: 05/17/2021] [Indexed: 01/12/2023]
Abstract
Advanced microscopy enables us to acquire quantities of time-lapse images to visualize the dynamic characteristics of tissues, cells or molecules. Microscopy images typically vary in signal-to-noise ratios and include a wealth of information which require multiple parameters and time-consuming iterative algorithms for processing. Precise analysis and statistical quantification are often needed for the understanding of the biological mechanisms underlying these dynamic image sequences, which has become a big challenge in the field. As deep learning technologies develop quickly, they have been applied in bioimage processing more and more frequently. Novel deep learning models based on convolution neural networks have been developed and illustrated to achieve inspiring outcomes. This review article introduces the applications of deep learning algorithms in microscopy image analysis, which include image classification, region segmentation, object tracking and super-resolution reconstruction. We also discuss the drawbacks of existing deep learning-based methods, especially on the challenges of training datasets acquisition and evaluation, and propose the potential solutions. Furthermore, the latest development of augmented intelligent microscopy that based on deep learning technology may lead to revolution in biomedical research.
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Affiliation(s)
- Zhichao Liu
- Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China
| | - Luhong Jin
- Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China
| | - Jincheng Chen
- Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China
| | - Qiuyu Fang
- Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China
| | - Sergey Ablameyko
- National Academy of Sciences, United Institute of Informatics Problems, Belarusian State University, Minsk, 220012, Belarus
| | - Zhaozheng Yin
- AI Institute, Department of Biomedical Informatics and Department of Computer Science, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Yingke Xu
- Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China; Department of Endocrinology, The Affiliated Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China.
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Salvi M, Mogetta A, Gambella A, Molinaro L, Barreca A, Papotti M, Molinari F. Automated assessment of glomerulosclerosis and tubular atrophy using deep learning. Comput Med Imaging Graph 2021; 90:101930. [PMID: 33964790 DOI: 10.1016/j.compmedimag.2021.101930] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 02/20/2021] [Accepted: 04/21/2021] [Indexed: 01/16/2023]
Abstract
In kidney transplantations, pathologists evaluate the architecture of both glomeruli, interstitium and tubules to assess the nephron status. An accurate assessment of glomerulosclerosis and tubular atrophy is crucial for determining kidney acceptance, which is currently based on the pathologists' histological evaluations on renal biopsies in addition to clinical data. In this work, we present an automated algorithm, called RENTAG (Robust EvaluatioN of Tubular Atrophy & Glomerulosclerosis), for the segmentation and classification of glomerular and tubular structures in histopathological images. The proposed novel strategy combines the accuracy of a level-set with the semantic segmentation of convolutional neural networks to detect the glomeruli and tubules contours. In the TEST set, our method exhibited excellent performance in both glomeruli (dice score: 0.9529) and tubule (dice score: 0.9174) detection and outperformed all the compared methods. To the best of our knowledge, the RENTAG algorithm is the first fully automated method capable of quantifying glomerulosclerosis and tubular atrophy in digital histological images. The developed software can be employed for the analysis of pre-transplantation biopsies to support the pathologists' diagnostic activity.
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Affiliation(s)
- Massimo Salvi
- Politecnico di Torino, PoliToBIOMed Lab, Biolab, Department of Electronics and Telecommunications, Corso Duca degli Abruzzi 24, Turin, 10129, Italy.
| | - Alessandro Mogetta
- Politecnico di Torino, PoliToBIOMed Lab, Biolab, Department of Electronics and Telecommunications, Corso Duca degli Abruzzi 24, Turin, 10129, Italy
| | - Alessandro Gambella
- Pathology Unit, Department of Medical Sciences, University of Turin, Via Santena 7, Turin, 10126, Italy
| | - Luca Molinaro
- A.O.U. Città della Salute e della Scienza Hospital, Division of Pathology, Corso Bramante 88, Turin, 10126, Italy
| | - Antonella Barreca
- A.O.U. Città della Salute e della Scienza Hospital, Division of Pathology, Corso Bramante 88, Turin, 10126, Italy
| | - Mauro Papotti
- University of Turin, Division of Pathology, Department of Oncology, Via Santena 7, Turin, 10126, Italy
| | - Filippo Molinari
- Politecnico di Torino, PoliToBIOMed Lab, Biolab, Department of Electronics and Telecommunications, Corso Duca degli Abruzzi 24, Turin, 10129, Italy
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Automated Detection of B Cell and T Cell Acute Lymphoblastic Leukaemia Using Deep Learning. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2021.05.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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Deshpande NM, Gite S, Aluvalu R. A review of microscopic analysis of blood cells for disease detection with AI perspective. PeerJ Comput Sci 2021; 7:e460. [PMID: 33981834 PMCID: PMC8080427 DOI: 10.7717/peerj-cs.460] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 03/06/2021] [Indexed: 05/07/2023]
Abstract
BACKGROUND Any contamination in the human body can prompt changes in blood cell morphology and various parameters of cells. The minuscule images of blood cells are examined for recognizing the contamination inside the body with an expectation of maladies and variations from the norm. Appropriate segmentation of these cells makes the detection of a disease progressively exact and vigorous. Microscopic blood cell analysis is a critical activity in the pathological analysis. It highlights the investigation of appropriate malady after exact location followed by an order of abnormalities, which assumes an essential job in the analysis of various disorders, treatment arranging, and assessment of results of treatment. METHODOLOGY A survey of different areas where microscopic imaging of blood cells is used for disease detection is done in this paper. Research papers from this area are obtained from a popular search engine, Google Scholar. The articles are searched considering the basics of blood such as its composition followed by staining of blood, that is most important and mandatory before microscopic analysis. Different methods for classification, segmentation of blood cells are reviewed. Microscopic analysis using image processing, computer vision and machine learning are the main focus of the analysis and the review here. Methodologies employed by different researchers for blood cells analysis in terms of these mentioned algorithms is the key point of review considered in the study. RESULTS Different methodologies used for microscopic analysis of blood cells are analyzed and are compared according to different performance measures. From the extensive review the conclusion is made. CONCLUSION There are different machine learning and deep learning algorithms employed by researchers for segmentation of blood cell components and disease detection considering microscopic analysis. There is a scope of improvement in terms of different performance evaluation parameters. Different bio-inspired optimization algorithms can be used for improvement. Explainable AI can analyze the features of AI implemented system and will make the system more trusted and commercially suitable.
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Affiliation(s)
- Nilkanth Mukund Deshpande
- Department of Electronics and Telecommunication, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India
- Electronics & Telecommunication Department, Dr. Vithalrao Vikhe Patil College of Engineering, Ahmednagar, Ahmednagar, Maharashtra, India
| | - Shilpa Gite
- Department of Computer Science, Symbiosis Institute of Technology, Pune Symbiosis International (Deemed University), Pune, Maharashtra, India
- Symbiosis Center for Applied Artificial Intelligence (SCAAI), Symbiosis International (Deemed University), Pune, Maharashtra, India
| | - Rajanikanth Aluvalu
- Department of CSE, Vardhaman College of Engineering, Hyderabad, Telangana, India
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Boldú L, Merino A, Acevedo A, Molina A, Rodellar J. A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 202:105999. [PMID: 33618145 DOI: 10.1016/j.cmpb.2021.105999] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 02/08/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVES Morphological differentiation among blasts circulating in blood in acute leukaemia is challenging. Artificial intelligence decision support systems hold substantial promise as part of clinical practise in detecting haematological malignancy. This study aims to develop a deep learning-based system to predict the diagnosis of acute leukaemia using blood cell images. METHODS A set of 731 blood smears containing 16,450 single-cell images was analysed from 100 healthy controls, 191 patients with viral infections and 148 with acute leukaemia. Training and testing sets were arranged with 85% and 15% of these smears, respectively. To find the best architecture for acute leukaemia classification VGG16, ResNet101, DenseNet121 and SENet154 were evaluated. Fine-tuning was implemented to these pre-trained CNNs to adapt their layers to our data. Once the best architecture was chosen, a system with two modules working sequentially was configured (ALNet). The first module recognised abnormal promyelocytes among other mononuclear blood cell images, such as lymphocytes, monocytes, reactive lymphocytes and blasts. The second distinguished if blasts were myeloid or lymphoid lineage. The final strategy was to predict patients' initial diagnosis of acute leukaemia lineage using the blood smear review. ALNet was assessed with smears of the testing set. RESULTS ALNet provided the correct diagnostic prediction of all patients with promyelocytic and myeloid leukaemia. Sensitivity, specificity and precision values of 100%, 92.3% and 93.7%, respectively, were obtained for myeloid leukaemia. Regarding lymphoid leukaemia, a sensitivity of 89% and specificity and precision values of 100% were obtained. CONCLUSIONS ALNet is a predictive model designed with two serially connected convolutional networks. It is proposed to assist clinical pathologists in the diagnosis of acute leukaemia during the blood smear review. It has been proved to distinguish neoplastic (leukaemia) and non-neoplastic (infections) diseases, as well as recognise the leukaemia lineage.
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Affiliation(s)
- Laura Boldú
- Hospital Clínic de Barcelona-IDIBAPS, Haematology and Cytology Unit, CORE Laboratory, Biomedical Diagnostic Centre, Spain
| | - Anna Merino
- Hospital Clínic de Barcelona-IDIBAPS, Haematology and Cytology Unit, CORE Laboratory, Biomedical Diagnostic Centre, Spain.
| | - Andrea Acevedo
- Hospital Clínic de Barcelona-IDIBAPS, Haematology and Cytology Unit, CORE Laboratory, Biomedical Diagnostic Centre, Spain; Technical University of Catalonia, Barcelona East Engineering School, Department of Mathematics, Spain
| | - Angel Molina
- Hospital Clínic de Barcelona-IDIBAPS, Haematology and Cytology Unit, CORE Laboratory, Biomedical Diagnostic Centre, Spain
| | - José Rodellar
- Technical University of Catalonia, Barcelona East Engineering School, Department of Mathematics, Spain
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IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:6648574. [PMID: 33343851 PMCID: PMC7732373 DOI: 10.1155/2020/6648574] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 11/02/2020] [Accepted: 11/21/2020] [Indexed: 12/13/2022]
Abstract
For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients' lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.
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26
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Berryman S, Matthews K, Lee JH, Duffy SP, Ma H. Image-based phenotyping of disaggregated cells using deep learning. Commun Biol 2020; 3:674. [PMID: 33188302 PMCID: PMC7666170 DOI: 10.1038/s42003-020-01399-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 10/20/2020] [Indexed: 12/14/2022] Open
Abstract
The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undesirable. Machine learning has been used for image cytometry but has been limited by cell agglomeration and it is currently unclear if this approach can reliably phenotype cells that are difficult to distinguish by the human eye. Here, we show disaggregated single cells can be phenotyped with a high degree of accuracy using low-resolution bright-field and non-specific fluorescence images of the nucleus, cytoplasm, and cytoskeleton. Specifically, we trained a convolutional neural network using automatically segmented images of cells from eight standard cancer cell-lines. These cells could be identified with an average F1-score of 95.3%, tested using separately acquired images. Our results demonstrate the potential to develop an "electronic eye" to phenotype cells directly from microscopy images.
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Affiliation(s)
- Samuel Berryman
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
- Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
| | - Kerryn Matthews
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
- Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
| | - Jeong Hyun Lee
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
- Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
| | - Simon P Duffy
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
- Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada
- British Columbia Institute of Technology, Burnaby, BC, Canada
| | - Hongshen Ma
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada.
- Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada.
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
- Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, BC, Canada.
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Sun J, Wang L, Liu Q, Tárnok A, Su X. Deep learning-based light scattering microfluidic cytometry for label-free acute lymphocytic leukemia classification. BIOMEDICAL OPTICS EXPRESS 2020; 11:6674-6686. [PMID: 33282516 PMCID: PMC7687967 DOI: 10.1364/boe.405557] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 10/14/2020] [Accepted: 10/14/2020] [Indexed: 05/27/2023]
Abstract
The subtyping of Acute lymphocytic leukemia (ALL) is important for proper treatment strategies and prognosis. Conventional methods for manual blood and bone marrow testing are time-consuming and labor-intensive, while recent flow cytometric immunophenotyping has the limitations such as high cost. Here we develop the deep learning-based light scattering imaging flow cytometry for label-free classification of ALL. The single ALL cells confined in three dimensional (3D) hydrodynamically focused stream are excited by light sheet. Our label-free microfluidic cytometry obtains big-data two dimensional (2D) light scattering patterns from single ALL cells of B/T subtypes. A deep learning framework named Inception V3-SIFT (Scale invariant feature transform)-Scattering Net (ISSC-Net) is developed, which can perform high-precision classification of T-ALL and B-ALL cell line cells with an accuracy of 0.993 ± 0.003. Our deep learning-based 2D light scattering flow cytometry is promising for automatic and accurate subtyping of un-stained ALL.
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Affiliation(s)
- Jing Sun
- School of Microelectronics, Shandong University, Jinan, China
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Lan Wang
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Qiao Liu
- Key Laboratory of Experimental Teratology (Ministry of Education); Department of Molecular Medicine and Genetics, School of Basic Medicine Sciences, Shandong University, Jinan, China
| | - Attila Tárnok
- Department of Therapy Validation, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
| | - Xuantao Su
- School of Microelectronics, Shandong University, Jinan, China
- Advanced Medical Research Institute, Shandong University, Jinan, China
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Anilkumar K, Manoj V, Sagi T. A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.08.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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29
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FAB classification of acute leukemia using an ensemble of neural networks. EVOLUTIONARY INTELLIGENCE 2020. [DOI: 10.1007/s12065-020-00491-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Ochoa-Montiel R, Olague G, Sossa H. Expert knowledge for the recognition of leukemic cells. APPLIED OPTICS 2020; 59:4448-4460. [PMID: 32400425 DOI: 10.1364/ao.385208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 04/15/2020] [Indexed: 06/11/2023]
Abstract
This work shows the advantage of expert knowledge for leukemic cell recognition. In the medical area, visual analysis of microscopic images has regularly used biological samples to recognize hematological disorders. Nowadays, techniques of image recognition are needed to achieve an adequate identification of blood tissues. This paper presents a procedure to acquire expert knowledge from blood cell images. We apply Gaussian mixtures, evolutionary computing, and standard techniques of image processing to extract knowledge. This information feeds a support vector machine or multilayer perceptron to classify healthy or leukemic cells. Additionally, convolutional neural networks are used as a benchmark to compare our proposed method with the state of the art. We use a public database of 260 healthy and leukemic cell images. Results show that our traditional pattern recognition methodology matches deep learning accuracy since the recognition of blood cells achieves 99.63%, whereas the convolutional neural networks reach 97.74% on average. Moreover, the computational effort of our approach is minimal, while meeting the requirement of being explainable.
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