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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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Barrera K, Rodellar J, Alférez S, Merino A. A deep learning approach for automatic recognition of abnormalities in the cytoplasm of neutrophils. Comput Biol Med 2024; 178:108691. [PMID: 38905894 DOI: 10.1016/j.compbiomed.2024.108691] [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: 04/03/2024] [Revised: 05/12/2024] [Accepted: 06/01/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND AND OBJECTIVES This study aims to develop and evaluate NeuNN, a system based on convolutional neural networks (CNN) and generative adversarial networks (GAN) for the automatic identification of normal neutrophils and those containing several types of inclusions or showing hypogranulation. METHODS From peripheral blood smears, a set of 5605 digital images was obtained with neutrophils belonging to seven categories: Normal neutrophils (NEU), Hypogranulated (HYP) or containing cryoglobulins (CRY), Döhle bodies (DB), Howell-Jolly body-like inclusions (HJBLI), Green-blue inclusions of death (GBI) and phagocytosed bacteria (BAC). The dataset utilized in this study has been made publicly available. The class of GBI was augmented using synthetic images generated by GAN. The NeuNN classification model is based on an EfficientNet-B7 architecture trained from scratch. RESULTS NeuNN achieved an overall performance of 94.3% accuracy on the test data set. Performance metrics, including sensitivity, specificity, precision, F1-Score, Jaccard index, and Matthews correlation coefficient indicated overall values of 94%, 99.1%, 94.3%, 94.3%, 89.6%, and 93.6%, respectively. CONCLUSIONS The proposed approach, combining data augmentation and classification techniques, allows for automated identification of morphological findings in neutrophils, such us inclusions or hypogranulation. The system can be used as a support tool for clinical pathologists to detect these specific abnormalities with clinical relevance.
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Affiliation(s)
- Kevin Barrera
- Technical University of Catalonia, Barcelona East Engineering School, Department of Mathematics, Barcelona, Spain.
| | - José Rodellar
- Technical University of Catalonia, Barcelona East Engineering School, Department of Mathematics, Barcelona, Spain.
| | - Santiago Alférez
- Technical University of Catalonia, Barcelona East Engineering School, Department of Mathematics, Barcelona, Spain.
| | - Anna Merino
- Hospital Clínic of Barcelona-IDIBAPS, Biochemistry and Molecular Genetics Department, CORE Laboratory, Biomedical Diagnostic, Barcelona, Spain.
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Yang SH, Huang CJ, Huang JS. Increasing Robustness of Intracortical Brain-Computer Interfaces for Recording Condition Changes via Data Augmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 251:108208. [PMID: 38754326 DOI: 10.1016/j.cmpb.2024.108208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 04/30/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND AND OBJECTIVE Intracortical brain-computer interfaces (iBCIs) aim to help paralyzed individuals restore their motor functions by decoding neural activity into intended movement. However, changes in neural recording conditions hinder the decoding performance of iBCIs, mainly because the neural-to-kinematic mappings shift. Conventional approaches involve either training the neural decoders using large datasets before deploying the iBCI or conducting frequent calibrations during its operation. However, collecting data for extended periods can cause user fatigue, negatively impacting the quality and consistency of neural signals. Furthermore, frequent calibration imposes a substantial computational load. METHODS This study proposes a novel approach to increase iBCIs' robustness against changing recording conditions. The approach uses three neural augmentation operators to generate augmented neural activity that mimics common recording conditions. Then, contrastive learning is used to learn latent factors by maximizing the similarity between the augmented neural activities. The learned factors are expected to remain stable despite varying recording conditions and maintain a consistent correlation with the intended movement. RESULTS Experimental results demonstrate that the proposed iBCI outperformed the state-of-the-art iBCIs and was robust to changing recording conditions across days for long-term use on one publicly available nonhuman primate dataset. It achieved satisfactory offline decoding performance, even when a large training dataset was unavailable. CONCLUSIONS This study paves the way for reducing the need for frequent calibration of iBCIs and collecting a large amount of annotated training data. Potential future works aim to improve offline decoding performance with an ultra-small training dataset and improve the iBCIs' robustness to severely disabled electrodes.
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Affiliation(s)
- Shih-Hung Yang
- Department of Mechanical Engineering, National Cheng Kung University, Tainan, 701, Taiwan.
| | - Chun-Jui Huang
- Department of Mechanical Engineering, National Cheng Kung University, Tainan, 701, Taiwan
| | - Jhih-Siang Huang
- Department of Mechanical Engineering, National Cheng Kung University, Tainan, 701, Taiwan
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Khan S, Sajjad M, Abbas N, Escorcia-Gutierrez J, Gamarra M, Muhammad K. Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network. Comput Biol Med 2024; 174:108146. [PMID: 38608320 DOI: 10.1016/j.compbiomed.2024.108146] [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: 09/07/2023] [Revised: 02/06/2024] [Accepted: 02/12/2024] [Indexed: 04/14/2024]
Abstract
Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists, aiding diagnosis across medical conditions. Traditional techniques, including manual counting, detection, classification, and visual inspection of microscopic images by medical professionals, pose challenges due to their labor-intensive nature. However, traditional methods are time consuming and sometimes susceptible to errors. Here, we propose a high-performance convolutional neural network (CNN) coupled with a dual-attention network that efficiently detects and classifies WBCs in microscopic thick smear images. The main aim of this study was to enhance clinical hematology systems and expedite medical diagnostic processes. In the proposed technique, we utilized a deep convolutional generative adversarial network (DCGAN) to overcome the limitations imposed by limited training data and employed a dual attention mechanism to improve accuracy, efficiency, and generalization. The proposed technique achieved overall accuracy rates of 99.83%, 99.35%, and 99.60% for the peripheral blood cell (PBC), leukocyte images for segmentation and classification (LISC), and Raabin-WBC benchmark datasets, respectively. Our proposed approach outperforms state-of-the-art methods in terms of accuracy, highlighting the effectiveness of the strategies employed and their potential to enhance diagnostic capabilities and advance real-world healthcare practices and diagnostic systems.
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Affiliation(s)
- Siraj Khan
- Digital Image Processing Laboratory (DIP Lab), Department of Computer Science, Islamia College University, Peshawar, 25120, Pakistan
| | - Muhammad Sajjad
- Digital Image Processing Laboratory (DIP Lab), Department of Computer Science, Islamia College University, Peshawar, 25120, Pakistan.
| | - Naveed Abbas
- Digital Image Processing Laboratory (DIP Lab), Department of Computer Science, Islamia College University, Peshawar, 25120, Pakistan
| | - José Escorcia-Gutierrez
- Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia
| | - Margarita Gamarra
- Department of System Engineering, Universidad del Norte, Puerto Colombia, 081007, Colombia
| | - Khan Muhammad
- Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, 03063, South Korea.
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Fan BE, Yong BSJ, Li R, Wang SSY, Aw MYN, Chia MF, Chen DTY, Neo YS, Occhipinti B, Ling RR, Ramanathan K, Ong YX, Lim KGE, Wong WYK, Lim SP, Latiff STBA, Shanmugam H, Wong MS, Ponnudurai K, Winkler S. From microscope to micropixels: A rapid review of artificial intelligence for the peripheral blood film. Blood Rev 2024; 64:101144. [PMID: 38016837 DOI: 10.1016/j.blre.2023.101144] [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: 10/06/2023] [Revised: 11/12/2023] [Accepted: 11/14/2023] [Indexed: 11/30/2023]
Abstract
Artificial intelligence (AI) and its application in classification of blood cells in the peripheral blood film is an evolving field in haematology. We performed a rapid review of the literature on AI and peripheral blood films, evaluating the condition studied, image datasets, machine learning models, training set size, testing set size and accuracy. A total of 283 studies were identified, encompassing 6 broad domains: malaria (n = 95), leukemia (n = 81), leukocytes (n = 72), mixed (n = 25), erythrocytes (n = 15) or Myelodysplastic syndrome (MDS) (n = 1). These publications have demonstrated high self-reported mean accuracy rates across various studies (95.5% for malaria, 96.0% for leukemia, 94.4% for leukocytes, 95.2% for mixed studies and 91.2% for erythrocytes), with an overall mean accuracy of 95.1%. Despite the high accuracy, the challenges toward real world translational usage of these AI trained models include the need for well-validated multicentre data, data standardisation, and studies on less common cell types and non-malarial blood-borne parasites.
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Affiliation(s)
- Bingwen Eugene Fan
- Department of Haematology, Tan Tock Seng Hospital, Singapore; Department of Laboratory Medicine, Khoo Teck Puat Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Bryan Song Jun Yong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Ruiqi Li
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | | | | | - Ming Fang Chia
- Department of Haematology, Tan Tock Seng Hospital, Singapore
| | | | - Yuan Shan Neo
- ASUS Intelligent Cloud Services, Singapore, Singapore
| | | | - Ryan Ruiyang Ling
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kollengode Ramanathan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Cardiothoracic Intensive Care Unit, National University Heart Centre, National University Hospital, Singapore, Singapore
| | - Yi Xiong Ong
- Department of Laboratory Medicine, Tan Tock Seng Hospital, Singapore
| | | | | | - Shu Ping Lim
- Department of Laboratory Medicine, Tan Tock Seng Hospital, Singapore
| | | | | | - Moh Sim Wong
- Department of Laboratory Medicine, Khoo Teck Puat Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kuperan Ponnudurai
- Department of Haematology, Tan Tock Seng Hospital, Singapore; Department of Laboratory Medicine, Khoo Teck Puat Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Stefan Winkler
- ASUS Intelligent Cloud Services, Singapore, Singapore; School of Computing, National University of Singapore, Singapore
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Yong MP, Hum YC, Lai KW, Lee YL, Goh CH, Yap WS, Tee YK. Histopathological Cancer Detection Using Intra-Domain Transfer Learning and Ensemble Learning. IEEE ACCESS 2024; 12:1434-1457. [DOI: 10.1109/access.2023.3343465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/26/2024]
Affiliation(s)
- Ming Ping Yong
- Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Malaysia
| | - Yan Chai Hum
- Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Ying Loong Lee
- Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Malaysia
| | - Choon-Hian Goh
- Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Malaysia
| | - Wun-She Yap
- Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Malaysia
| | - Yee Kai Tee
- Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Malaysia
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Barrera K, Rodellar J, Alférez S, Merino A. Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107629. [PMID: 37301181 DOI: 10.1016/j.cmpb.2023.107629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/23/2023] [Accepted: 05/28/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVES Combining knowledge of clinical pathologists and deep learning models is a growing trend in morphological analysis of cells circulating in blood to add objectivity, accuracy, and speed in diagnosing hematological and non-hematological diseases. However, the variability in staining protocols across different laboratories can affect the color of images and performance of automatic recognition models. The objective of this work is to develop, train and evaluate a new system for the normalization of color staining of peripheral blood cell images, so that it transforms images from different centers to map the color staining of a reference center (RC) while preserving the structural morphological features. METHODS The system has two modules, GAN1 and GAN2. GAN1 uses the PIX2PIX technique to fade original color images to an adaptive gray, while GAN2 transforms them into RGB normalized images. Both GANs have a similar structure, where the generator is a U-NET convolutional neural network with ResNet and the discriminator is a classifier with ResNet34 structure. Digitally stained images were evaluated using GAN metrics and histograms to assess the ability to modify color without altering cell morphology. The system was also evaluated as a pre-processing tool before cells undergo a classification process. For this purpose, a CNN classifier was designed for three classes: abnormal lymphocytes, blasts and reactive lymphocytes. RESULTS Training of all GANs and the classifier was performed using RC images, while evaluations were conducted using images from four other centers. Classification tests were performed before and after applying the stain normalization system. The overall accuracy reached a similar value around 96% in both cases for the RC images, indicating the neutrality of the normalization model for the reference images. On the contrary, it was a significant improvement in the classification performance when applying the stain normalization to the other centers. Reactive lymphocytes were the most sensitive to stain normalization, with true positive rates (TPR) increasing from 46.3% - 66% for the original images to 81.2% - 97.2% after digital staining. Abnormal lymphocytes TPR ranged from 31.9% - 95.7% with original images to 83% - 100% with digitally stained images. Blast class showed TPR ranges of 90.3% - 94.4% and 94.4% - 100%, for original and stained images, respectively. CONCLUSIONS The proposed GAN-based normalization staining approach improves the performance of classifiers with multicenter data sets by generating digitally stained images with a quality similar to the original images and adaptability to a reference staining standard. The system requires low computation cost and can help improve the performance of automatic recognition models in clinical settings.
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Affiliation(s)
- Kevin Barrera
- Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona East Engineering School, Department of Mathematics, Barcelona, Spain.
| | - José Rodellar
- Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona East Engineering School, Department of Mathematics, Barcelona, Spain.
| | - Santiago Alférez
- Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona East Engineering School, Department of Mathematics, Barcelona, Spain.
| | - Anna Merino
- Hospital Clínic of Barcelona-IDIBAPS, Biochemistry and Molecular Genetics Department, CORE Laboratory, Biomedical Diagnostic, Barcelona, Spain.
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