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Fang T, Huang X, Chen X, Chen D, Wang J, Chen J. Segmentation, feature extraction and classification of leukocytes leveraging neural networks, a comparative study. Cytometry A 2024; 105:536-546. [PMID: 38420862 DOI: 10.1002/cyto.a.24832] [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: 05/21/2023] [Revised: 02/02/2024] [Accepted: 02/19/2024] [Indexed: 03/02/2024]
Abstract
The gold standard of leukocyte differentiation is a manual examination of blood smears, which is not only time and labor intensive but also susceptible to human error. As to automatic classification, there is still no comparative study of cell segmentation, feature extraction, and cell classification, where a variety of machine and deep learning models are compared with home-developed approaches. In this study, both traditional machine learning of K-means clustering versus deep learning of U-Net, U-Net + ResNet18, and U-Net + ResNet34 were used for cell segmentation, producing segmentation accuracies of 94.36% versus 99.17% for the dataset of CellaVision and 93.20% versus 98.75% for the dataset of BCCD, confirming that deep learning produces higher performance than traditional machine learning in leukocyte classification. In addition, a series of deep-learning approaches, including AlexNet, VGG16, and ResNet18, was adopted to conduct feature extraction and cell classification of leukocytes, producing classification accuracies of 91.31%, 97.83%, and 100% of CellaVision as well as 81.18%, 91.64% and 97.82% of BCCD, confirming the capability of the increased deepness of neural networks in leukocyte classification. As to the demonstrations, this study further conducted cell-type classification of ALL-IDB2 and PCB-HBC datasets, producing high accuracies of 100% and 98.49% among all literature, validating the deep learning model used in this study.
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Affiliation(s)
- Tingxuan Fang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, People's Republic of China
- School of Electronic, Electrical and Communication Engineering of University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Xukun Huang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Electronic, Electrical and Communication Engineering of University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Xiao Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Deyong Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Electronic, Electrical and Communication Engineering of University of Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Junbo Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Electronic, Electrical and Communication Engineering of University of Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Jian Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Electronic, Electrical and Communication Engineering of University of Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, People's Republic of China
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2
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Anand V, Gupta S, Koundal D, Alghamdi WY, Alsharbi BM. Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology. BMC Med Imaging 2024; 24:83. [PMID: 38589793 PMCID: PMC11003052 DOI: 10.1186/s12880-024-01254-z] [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: 02/15/2024] [Accepted: 03/19/2024] [Indexed: 04/10/2024] Open
Abstract
The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. The leukocyte dataset comprises four classes of images such as monocytes, lymphocytes, eosinophils, and neutrophils. Leukocyte segmentation is achieved through image processing techniques, including background subtraction, noise removal, and contouring. To get isolated leukocytes, background mask creation, Erythrocytes mask creation, and Leukocytes mask creation are performed on the blood cell images. Isolated leukocytes are then subjected to data augmentation including brightness and contrast adjustment, flipping, and random shearing, to improve the generalizability of the CNN model. A deep Convolutional Neural Network (CNN) model is employed on augmented dataset for effective feature extraction and classification. The deep CNN model consists of four convolutional blocks having eleven convolutional layers, eight batch normalization layers, eight Rectified Linear Unit (ReLU) layers, and four dropout layers to capture increasingly complex patterns. For this research, a publicly available dataset from Kaggle consisting of a total of 12,444 images of four types of leukocytes was used to conduct the experiments. Results showcase the robustness of the proposed framework, achieving impressive performance metrics with an accuracy of 97.98% and precision of 97.97%. These outcomes affirm the efficacy of the devised segmentation and classification approach in accurately identifying and categorizing leukocytes. The combination of advanced CNN architecture and meticulous pre-processing steps establishes a foundation for future developments in the field of medical image analysis.
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Affiliation(s)
- Vatsala Anand
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
| | - Sheifali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
| | - Deepika Koundal
- School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.
- Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam.
| | - Wael Y Alghamdi
- Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, 21944, Taif, Saudi Arabia
| | - Bayan M Alsharbi
- Department of Information Technology, College of Computers and Information Technology, Taif University, P. O. Box 11099, 21944, Taif, Saudi Arabia
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Jiang Y, Shen Y, Wang Y, Ding Q. Automatic recognition of white blood cell images with memory efficient superpixel metric GNN: SMGNN. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:2163-2188. [PMID: 38454678 DOI: 10.3934/mbe.2024095] [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: 03/09/2024]
Abstract
An automatic recognizing system of white blood cells can assist hematologists in the diagnosis of many diseases, where accuracy and efficiency are paramount for computer-based systems. In this paper, we presented a new image processing system to recognize the five types of white blood cells in peripheral blood with marked improvement in efficiency when juxtaposed against mainstream methods. The prevailing deep learning segmentation solutions often utilize millions of parameters to extract high-level image features and neglect the incorporation of prior domain knowledge, which consequently consumes substantial computational resources and increases the risk of overfitting, especially when limited medical image samples are available for training. To address these challenges, we proposed a novel memory-efficient strategy that exploits graph structures derived from the images. Specifically, we introduced a lightweight superpixel-based graph neural network (GNN) and broke new ground by introducing superpixel metric learning to segment nucleus and cytoplasm. Remarkably, our proposed segmentation model superpixel metric graph neural network (SMGNN) achieved state of the art segmentation performance while utilizing at most 10000$ \times $ less than the parameters compared to existing approaches. The subsequent segmentation-based cell type classification processes showed satisfactory results that such automatic recognizing algorithms are accurate and efficient to execeute in hematological laboratories. Our code is publicly available at https://github.com/jyh6681/SPXL-GNN.
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Affiliation(s)
- Yuanhong Jiang
- School of Mathematical Sciences, MOE-LSC, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Yiqing Shen
- Department of Computer Science, Johns Hopkins University, USA
| | - Yuguang Wang
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200030, China
- Shanghai Artificial Intelligence Laboratory, Shanghai 200433, China
| | - Qiaoqiao Ding
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200030, China
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4
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Moradi N, Haji Mohamad Hoseyni F, Hajghassem H, Yarahmadi N, Niknam Shirvan H, Safaie E, Kalantar M, Sefidbakht S, Amini A, Eeltink S. Comprehensive quantitative analysis of erythrocytes and leukocytes using trace volume of human blood using microfluidic-image cytometry and machine learning. LAB ON A CHIP 2023; 23:4868-4875. [PMID: 37867384 DOI: 10.1039/d3lc00692a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Abstract
A diagnostic test based on microfluidic image cytometry and machine learning has been designed and applied for accurate classification of erythrocytes and leukocytes, including a unique fully-automated 5-part quantitative differentiation into neutrophils, lymphocytes, monocytes, eosinophils, and basophils, using minute amounts of whole blood in a single counting chamber. A low-cost disposable multilayer microdevice for microfluidic image cytometry was developed that comprises a 1 mm × 22 mm × 70 μm (w × l × h) rectangular microchannel, allowing the analysis of trace volume of blood (20 μL) for each assay. Automated analysis of digitized binary images applying a border following algorithm was performed allowing the qualitative analysis of erythrocytes. Bright-field imaging was used for the detection of erythrocytes and fluorescence imaging for 5-part differentiation of leukocytes after acridine orange staining, applying a convolutional neural network enabling unparalleled speed for identification and automated morphology classification yielding 98.57% accuracy. Blood samples were obtained from 30 volunteers and count values did not significantly differ from data obtained using a commercial automated hematology analyzer.
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Affiliation(s)
- Nima Moradi
- University of Tehran, Faculty of New Sciences and Technologies, North Kargar Street, Tehran, Iran.
| | | | - Hassan Hajghassem
- University of Tehran, Faculty of New Sciences and Technologies, North Kargar Street, Tehran, Iran.
| | - Navid Yarahmadi
- University of Tehran, Faculty of New Sciences and Technologies, North Kargar Street, Tehran, Iran.
| | - Hadi Niknam Shirvan
- University of Tehran, Faculty of New Sciences and Technologies, North Kargar Street, Tehran, Iran.
| | - Erfan Safaie
- University of Tehran, Faculty of New Sciences and Technologies, North Kargar Street, Tehran, Iran.
| | - Mahsa Kalantar
- University of Tehran, Faculty of New Sciences and Technologies, North Kargar Street, Tehran, Iran.
| | | | - Ali Amini
- Vrije Universiteit Brussel, Department of Chemical Engineering, Brussels, Belgium
| | - Sebastiaan Eeltink
- Vrije Universiteit Brussel, Department of Chemical Engineering, Brussels, Belgium
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Huang M, Wang T, Cai Y, Fan H, Li Z. StainGAN: Learning a structural preserving translation for white blood cell images. JOURNAL OF BIOPHOTONICS 2023; 16:e202300196. [PMID: 37496209 DOI: 10.1002/jbio.202300196] [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: 05/28/2023] [Revised: 07/08/2023] [Accepted: 07/22/2023] [Indexed: 07/28/2023]
Abstract
Analysis of white blood cells in blood smear images plays a vital role in computer-aided diagnosis for the analysis and treatment of many diseases. However, different techniques for blood smear preparation result in images with large appearance variations, which limits the performance of large-scale machine learning algorithms. In this paper, we propose StainGAN, an image translation framework to transform the conventional Wright-stained white blood cell images into their rapidly-stained counterpart. Moreover, we designed a cluster-based learning strategy that does not require manual annotations and a multi-scale discriminator that incorporates a richer hierarchy of the spatial context to generate sharper images with better semantic consistency. Experimental results on multiple real-world datasets prove the effectiveness of our proposed framework. Moreover, we show that the transformed images from StainGAN can be used to boost the downstream segmentation performance under the label-limiting scenario.
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Affiliation(s)
- Maoye Huang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
| | - Tao Wang
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China
| | - Yuanzheng Cai
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China
| | - Haoyi Fan
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China
| | - Zuoyong Li
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China
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Zhong Y, Dan Y, Cai Y, Lin J, Huang X, Mahmoud O, Hald ES, Kumar A, Fang Q, Mahmoud SS. Efficient Malaria Parasite Detection From Diverse Images of Thick Blood Smears for Cross-Regional Model Accuracy. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 4:226-233. [PMID: 38059069 PMCID: PMC10697288 DOI: 10.1109/ojemb.2023.3328435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/22/2023] [Accepted: 10/24/2023] [Indexed: 12/08/2023] Open
Abstract
Goal: The purpose of this work is to improve malaria diagnosis efficiency by integrating smartphones with microscopes. This integration involves image acquisition and algorithmic detection of malaria parasites in various thick blood smear (TBS) datasets sourced from different global regions, including low-quality images from Sub-Saharan Africa. Methods: This approach combines image segmentation and a convolutional neural network (CNN) to distinguish between white blood cells, artifacts, and malaria parasites. A portable system integrates a microscope with a graphical user interface to facilitate rapid malaria detection from smartphone images. We trained the CNN model using open-source data from the Chittagong Medical College Hospital, Bangladesh. Results: The validation process, using microscopic TBS from both the training dataset and an additional dataset from Sub-Saharan Africa, demonstrated that the proposed model achieved an accuracy of 97.74% ± 0.05% and an F1-score of 97.75% ± 0.04%. Remarkably, our proposed model with AlexNet surpasses the reported literature performance of 96.32%. Conclusions: This algorithm shows promise in aiding malaria-stricken regions, especially those with limited resources.
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Affiliation(s)
- Yuming Zhong
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
| | - Ying Dan
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
| | - Yin Cai
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
| | - Jiamin Lin
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
| | - Xiaoyao Huang
- Shantou University Medical CollegeShantou UniversityShantou515063China
| | | | - Eric S. Hald
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
| | - Akshay Kumar
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
| | - Qiang Fang
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
| | - Seedahmed S. Mahmoud
- Department of Biomedical Engineering, College of EngineeringShantou UniversityShantou515063China
- The Frontier Technology Research InstituteFirst Affiliated Hospital of Shantou UniversityShantou515063China
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7
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Sheikh IM, Chachoo MA. A hybrid cell image segmentation method based on the multilevel improvement of data. Tissue Cell 2023; 84:102169. [PMID: 37499320 DOI: 10.1016/j.tice.2023.102169] [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: 02/22/2023] [Revised: 07/12/2023] [Accepted: 07/14/2023] [Indexed: 07/29/2023]
Abstract
Over the years, several methods have been developed for the segmentation of cell images. Most of the related techniques operate directly on the raw data (noisy cell samples) of the medical image which leads to adverse effects on the structure of leucocytes because the medical images are affected by multiple distortions (varying illumination, deficient background light intensity, and non-uniform staining). To overcome these problems, we came up with an improved solution that performs the qualitative enhancement of cell images for the smooth extraction of cell-nucleus. Although various segmentation methods have adopted an image improvement operation in practice. These methods also amplify the magnitude of image noise which leads to over-sampling and under-sampling of data points. This mis-labelling of data points is minimized by the developed approach which adopts a collaborative fusion strategy (CNN and Nuclear-norm approach) for the qualitative improvement of cell images. The enhanced cell samples were forwarded to the U-net (deep learning model) model for the semantic segmentation of cell images. The performance evaluation of the model was performed on three biomedical cell imaging datasets, which include the ALL-IDB (99.89% accuracy, 99.51% recall, and 99.01% precision), CellaVision (99.68% accuracy, 98.75% precision, and 97.94% specificity) and JTSC (98.45% accuracy, 97.42% precision, and 97.21% specificity) dataset. The results were compared with the state-of-art methods in which the adopted hybrid approach has overpowered the related techniques in the quantitative and qualitative domains.
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Affiliation(s)
- Ishfaq Majeed Sheikh
- University of Kashmir, Department of Computer Science, Hazratbal, Srinagar 190006, India.
| | - Manzoor Ahmad Chachoo
- University of Kashmir, Department of Computer Science, Hazratbal, Srinagar 190006, India
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Luo Y, Wang Y, Zhao Y, Guan W, Shi H, Fu C, Jiang H. A lightweight network based on dual-stream feature fusion and dual-domain attention for white blood cells segmentation. Front Oncol 2023; 13:1223353. [PMID: 37731631 PMCID: PMC10507331 DOI: 10.3389/fonc.2023.1223353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 08/04/2023] [Indexed: 09/22/2023] Open
Abstract
Introduction Accurate white blood cells segmentation from cytopathological images is crucial for evaluating leukemia. However, segmentation is difficult in clinical practice. Given the very large numbers of cytopathological images to be processed, diagnosis becomes cumbersome and time consuming, and diagnostic accuracy is also closely related to experts' experience, fatigue and mood and so on. Besides, fully automatic white blood cells segmentation is challenging for several reasons. There exists cell deformation, blurred cell boundaries, and cell color differences, cells overlapping or adhesion. Methods The proposed method improves the feature representation capability of the network while reducing parameters and computational redundancy by utilizing the feature reuse of Ghost module to reconstruct a lightweight backbone network. Additionally, a dual-stream feature fusion network (DFFN) based on the feature pyramid network is designed to enhance detailed information acquisition. Furthermore, a dual-domain attention module (DDAM) is developed to extract global features from both frequency and spatial domains simultaneously, resulting in better cell segmentation performance. Results Experimental results on ALL-IDB and BCCD datasets demonstrate that our method outperforms existing instance segmentation networks such as Mask R-CNN, PointRend, MS R-CNN, SOLOv2, and YOLACT with an average precision (AP) of 87.41%, while significantly reducing parameters and computational cost. Discussion Our method is significantly better than the current state-of-the-art single-stage methods in terms of both the number of parameters and FLOPs, and our method has the best performance among all compared methods. However, the performance of our method is still lower than the two-stage instance segmentation algorithms. in future work, how to design a more lightweight network model while ensuring a good accuracy will become an important problem.
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Affiliation(s)
- Yang Luo
- School of Mathematics and Information Science, Anshan Normal University, Anshan, Liaoning, China
| | - Yingwei Wang
- School of Mathematics and Information Science, Anshan Normal University, Anshan, Liaoning, China
| | - Yongda Zhao
- School of Mathematics and Information Science, Anshan Normal University, Anshan, Liaoning, China
| | - Wei Guan
- School of Applied Technology, Anshan Normal University, Anshan, Liaoning, China
| | - Hanfeng Shi
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Chong Fu
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
- Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Hongyang Jiang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
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Bodzas A, Kodytek P, Zidek J. A high-resolution large-scale dataset of pathological and normal white blood cells. Sci Data 2023; 10:466. [PMID: 37468490 PMCID: PMC10356748 DOI: 10.1038/s41597-023-02378-7] [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: 05/05/2023] [Accepted: 07/11/2023] [Indexed: 07/21/2023] Open
Abstract
Microscopic examination plays a significant role in the initial screening for a variety of hematological, as well as non-hematological, diagnoses. Microscopic blood smear examination that is considered a key diagnostic technique, is in recent clinical practice still performed manually, which is not only time consuming, but can lead to human errors. Although automated and semi-automated systems have been developed in recent years, their high purchasing and maintenance costs make them unaffordable for many medical institutions. Even though much research has been conducted lately to explore more accurate and feasible solutions, most researchers had to deal with a lack of medical data. To address the lack of large-scale databases in this field, we created a high-resolution dataset containing a total of 16027 annotated white blood cells. Moreover, the dataset covers overall 9 types of white blood cells, including clinically significant pathological findings. Since we used high-quality acquisition equipment, the dataset provides one of the highest quality images of blood cells, achieving an approximate resolution of 42 pixels per 1 μm.
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Affiliation(s)
- Alexandra Bodzas
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic.
| | - Pavel Kodytek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic
| | - Jan Zidek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic
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Diaz Resendiz JL, Ponomaryov V, Reyes Reyes R, Sadovnychiy S. Explainable CAD System for Classification of Acute Lymphoblastic Leukemia Based on a Robust White Blood Cell Segmentation. Cancers (Basel) 2023; 15:3376. [PMID: 37444486 DOI: 10.3390/cancers15133376] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
Leukemia is a significant health challenge, with high incidence and mortality rates. Computer-aided diagnosis (CAD) has emerged as a promising approach. However, deep-learning methods suffer from the "black box problem", leading to unreliable diagnoses. This research proposes an Explainable AI (XAI) Leukemia classification method that addresses this issue by incorporating a robust White Blood Cell (WBC) nuclei segmentation as a hard attention mechanism. The segmentation of WBC is achieved by combining image processing and U-Net techniques, resulting in improved overall performance. The segmented images are fed into modified ResNet-50 models, where the MLP classifier, activation functions, and training scheme have been tested for leukemia subtype classification. Additionally, we add visual explainability and feature space analysis techniques to offer an interpretable classification. Our segmentation algorithm achieves an Intersection over Union (IoU) of 0.91, in six databases. Furthermore, the deep-learning classifier achieves an accuracy of 99.9% on testing. The Grad CAM methods and clustering space analysis confirm improved network focus when classifying segmented images compared to non-segmented images. Overall, the proposed visual explainable CAD system has the potential to assist physicians in diagnosing leukemia and improving patient outcomes.
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Affiliation(s)
- Jose Luis Diaz Resendiz
- Instituto Politecnico Nacional, Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Av. Sta. Ana 1000, Mexico City 04440, Mexico
| | - Volodymyr Ponomaryov
- Instituto Politecnico Nacional, Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Av. Sta. Ana 1000, Mexico City 04440, Mexico
| | - Rogelio Reyes Reyes
- Instituto Politecnico Nacional, Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Av. Sta. Ana 1000, Mexico City 04440, Mexico
| | - Sergiy Sadovnychiy
- Instituto Mexicano del Petroleo, Eje Central Lazaro Cardenas Norte 152, Mexico City 07730, Mexico
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11
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Olayah F, Senan EM, Ahmed IA, Awaji B. Blood Slide Image Analysis to Classify WBC Types for Prediction Haematology Based on a Hybrid Model of CNN and Handcrafted Features. Diagnostics (Basel) 2023; 13:diagnostics13111899. [PMID: 37296753 DOI: 10.3390/diagnostics13111899] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
White blood cells (WBCs) are one of the main components of blood produced by the bone marrow. WBCs are part of the immune system that protects the body from infectious diseases and an increase or decrease in the amount of any type that causes a particular disease. Thus, recognizing the WBC types is essential for diagnosing the patient's health and identifying the disease. Analyzing blood samples to determine the amount and WBC types requires experienced doctors. Artificial intelligence techniques were applied to analyze blood samples and classify their types to help doctors distinguish between types of infectious diseases due to increased or decreased WBC amounts. This study developed strategies for analyzing blood slide images to classify WBC types. The first strategy is to classify WBC types by the SVM-CNN technique. The second strategy for classifying WBC types is by SVM based on hybrid CNN features, which are called VGG19-ResNet101-SVM, ResNet101-MobileNet-SVM, and VGG19-ResNet101-MobileNet-SVM techniques. The third strategy for classifying WBC types by FFNN is based on a hybrid model of CNN and handcrafted features. With MobileNet and handcrafted features, FFNN achieved an AUC of 99.43%, accuracy of 99.80%, precision of 99.75%, specificity of 99.75%, and sensitivity of 99.68%.
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Affiliation(s)
- Fekry Olayah
- Department of Information System, Faculty Computer Science and information System, Najran University, Najran 66462, Saudi Arabia
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen
| | | | - Bakri Awaji
- Department of Computer Science, Faculty of Computer Science and Information System, Najran University, Najran 66462, Saudi Arabia
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Liu X, Han T, Zhou C, Huang J, Ju M, Xu B, Song L. Low sampling high quality image reconstruction and segmentation based on array network ghost imaging. OPTICS EXPRESS 2023; 31:9945-9960. [PMID: 37157558 DOI: 10.1364/oe.481995] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
High-quality imaging under low sampling time is an important step in the practical application of computational ghost imaging (CGI). At present, the combination of CGI and deep learning has achieved ideal results. However, as far as we know, most researchers focus on one single pixel CGI based on deep learning, and the combination of array detection CGI and deep learning with higher imaging performance has not been mentioned. In this work, we propose a novel multi-task CGI detection method based on deep learning and array detector, which can directly extract target features from one-dimensional bucket detection signals at low sampling times, especially output high-quality reconstruction and image-free segmentation results at the same time. And this method can realize fast light field modulation of modulation devices such as digital micromirror device to improve the imaging efficiency by binarizing the trained floating-point spatial light field and fine-tuning the network. Meanwhile, the problem of partial information loss in the reconstructed image due to the detection unit gap in the array detector has also been solved. Simulation and experimental results show that our method can simultaneously obtain high-quality reconstructed and segmented images at sampling rate of 0.78 %. Even when the signal-to-noise ratio of the bucket signal is 15 dB, the details of the output image are still clear. This method helps to improve the applicability of CGI and can be applied to resource-constrained multi-task detection scenarios such as real-time detection, semantic segmentation, and object recognition.
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13
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Zhou X, Tong T, Zhong Z, Fan H, Li Z. Saliency-CCE: Exploiting colour contextual extractor and saliency-based biomedical image segmentation. Comput Biol Med 2023; 154:106551. [PMID: 36716685 DOI: 10.1016/j.compbiomed.2023.106551] [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: 03/13/2022] [Revised: 01/03/2023] [Accepted: 01/11/2023] [Indexed: 01/21/2023]
Abstract
Biomedical image segmentation is one critical component in computer-aided system diagnosis. However, various non-automatic segmentation methods are usually designed to segment target objects with single-task driven, ignoring the potential contribution of multi-task, such as the salient object detection (SOD) task and the image segmentation task. In this paper, we propose a novel dual-task framework for white blood cell (WBC) and skin lesion (SL) saliency detection and segmentation in biomedical images, called Saliency-CCE. Saliency-CCE consists of a preprocessing of hair removal for skin lesions images, a novel colour contextual extractor (CCE) module for the SOD task and an improved adaptive threshold (AT) paradigm for the image segmentation task. In the SOD task, we perform the CCE module to extract hand-crafted features through a novel colour channel volume (CCV) block and a novel colour activation mapping (CAM) block. We first exploit the CCV block to generate a target object's region of interest (ROI). After that, we employ the CAM block to yield a refined salient map as the final salient map from the extracted ROI. We propose a novel adaptive threshold (AT) strategy in the segmentation task to automatically segment the WBC and SL from the final salient map. We evaluate our proposed Saliency-CCE on the ISIC-2016, the ISIC-2017, and the SCISC datasets, which outperform representative state-of-the-art SOD and biomedical image segmentation approaches. Our code is available at https://github.com/zxg3017/Saliency-CCE.
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Affiliation(s)
- Xiaogen Zhou
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, P.R. China; College of Physics and Information Engineering, Fuzhou University, Fuzhou, P.R. China
| | - Tong Tong
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, P.R. China
| | - Zhixiong Zhong
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, P.R. China
| | - Haoyi Fan
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, P.R. China
| | - Zuoyong Li
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, P.R. China.
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14
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Wang Y, Ma R, Ma X, Cui H, Xiao Y, Wu X, Zhou Y. Shape-aware fine-grained classification of erythroid cells. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04465-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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15
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Li M, Lin C, Ge P, Li L, Song S, Zhang H, Lu L, Liu X, Zheng F, Zhang S, Sun X. A deep learning model for detection of leukocytes under various interference factors. Sci Rep 2023; 13:2160. [PMID: 36750590 PMCID: PMC9905612 DOI: 10.1038/s41598-023-29331-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 02/02/2023] [Indexed: 02/09/2023] Open
Abstract
The accurate detection of leukocytes is the basis for the diagnosis of blood system diseases. However, diagnosing leukocyte disorders by doctors is time-consuming and requires extensive experience. Automated detection methods with high accuracy can improve detection efficiency and provide recommendations to inexperienced doctors. Current methods and instruments either fail to automate the identification process fully or have low performance and need suitable leukocyte data sets for further study. To improve the current status, we need to develop more intelligent strategies. This paper investigates fulfilling high-performance automatic detection for leukocytes using a deep learning-based method. We established a new dataset more suitable for leukocyte detection, containing 6273 images (8595 leukocytes) and considering nine common clinical interference factors. Based on the dataset, the performance evaluation of six mainstream detection models is carried out, and a more robust ensemble model is proposed. The mean of average precision (mAP) @IoU = 0.50:0.95 and mean of average recall (mAR)@IoU = 0.50:0.95 of the ensemble model on the test set are 0.853 and 0.922, respectively. The detection performance of poor-quality images is robust. For the first time, it is found that the ensemble model yields an accuracy of 98.84% for detecting incomplete leukocytes. In addition, we also compared the test results of different models and found multiple identical false detections of the models, then provided correct suggestions for the clinic.
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Affiliation(s)
- Meiyu Li
- Tianjin Cancer Hospital Airport Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Cong Lin
- School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai, China
| | - Peng Ge
- Tianjin Cancer Hospital Airport Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Lei Li
- Clinical Laboratory, Tianjin Chest Hospital, Tianjin, China
| | - Shuang Song
- Tianjin Cancer Hospital Airport Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Hanshan Zhang
- The Australian National University, Canberra, Australia
| | - Lu Lu
- Institute of Disaster Medicine, Tianjin University, Tianjin, China
| | - Xiaoxiang Liu
- School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai, China
| | - Fang Zheng
- School of Medical Laboratory, Tianjin Medical University, Tianjin, China
| | - Shijie Zhang
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
| | - Xuguo Sun
- School of Medical Laboratory, Tianjin Medical University, Tianjin, China.
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16
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Zhou R, Ou Y, Fang X, Azarpazhooh MR, Gan H, Ye Z, Spence JD, Xu X, Fenster A. Ultrasound carotid plaque segmentation via image reconstruction-based self-supervised learning with limited training labels. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:1617-1636. [PMID: 36899501 DOI: 10.3934/mbe.2023074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Carotid total plaque area (TPA) is an important contributing measurement to the evaluation of stroke risk. Deep learning provides an efficient method for ultrasound carotid plaque segmentation and TPA quantification. However, high performance of deep learning requires datasets with many labeled images for training, which is very labor-intensive. Thus, we propose an image reconstruction-based self-supervised learning algorithm (IR-SSL) for carotid plaque segmentation when few labeled images are available. IR-SSL consists of pre-trained and downstream segmentation tasks. The pre-trained task learns region-wise representations with local consistency by reconstructing plaque images from randomly partitioned and disordered images. The pre-trained model is then transferred to the segmentation network as the initial parameters in the downstream task. IR-SSL was implemented with two networks, UNet++ and U-Net, and evaluated on two independent datasets of 510 carotid ultrasound images from 144 subjects at SPARC (London, Canada) and 638 images from 479 subjects at Zhongnan hospital (Wuhan, China). Compared to the baseline networks, IR-SSL improved the segmentation performance when trained on few labeled images (n = 10, 30, 50 and 100 subjects). For 44 SPARC subjects, IR-SSL yielded Dice-similarity-coefficients (DSC) of 80.14-88.84%, and algorithm TPAs were strongly correlated (r=0.962-0.993, p < 0.001) with manual results. The models trained on the SPARC images but applied to the Zhongnan dataset without retraining achieved DSCs of 80.61-88.18% and strong correlation with manual segmentation (r=0.852-0.978, p < 0.001). These results suggest that IR-SSL could improve deep learning when trained on small labeled datasets, making it useful for monitoring carotid plaque progression/regression in clinical use and trials.
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Affiliation(s)
- Ran Zhou
- School of Computer Science, Hubei University of Technology, Wuhan, China
| | - Yanghan Ou
- School of Computer Science, Hubei University of Technology, Wuhan, China
| | - Xiaoyue Fang
- School of Computer Science, Hubei University of Technology, Wuhan, China
| | | | - Haitao Gan
- School of Computer Science, Hubei University of Technology, Wuhan, China
| | - Zhiwei Ye
- School of Computer Science, Hubei University of Technology, Wuhan, China
| | - J David Spence
- Robarts Research Institute, Western University, London, Canada
| | - Xiangyang Xu
- Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Aaron Fenster
- Robarts Research Institute, Western University, London, Canada
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17
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Noshad A, Fallahi S. A new hybrid framework based on deep neural networks and JAYA optimization algorithm for feature selection using SVM applied to classification of acute lymphoblastic Leukaemia. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2157748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Ali Noshad
- Department of Engineering, Polytechnic University of Milan, Milan, Italy
| | - Saeed Fallahi
- Department of Mathematics, Salman Farsi University of Kazerun, Kazerun, Iran
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18
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Tamang T, Baral S, Paing MP. Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12122903. [PMID: 36552910 PMCID: PMC9777002 DOI: 10.3390/diagnostics12122903] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 11/24/2022] Open
Abstract
White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. Traditionally, the clinical laboratory procedure for quantifying the specific types of white blood cells is an integral part of a complete blood count (CBC) test, which aids in monitoring the health of people. With the advancements in deep learning, blood film images can be classified in less time and with high accuracy using various algorithms. This paper exploits a number of state-of-the-art deep learning models and their variations based on CNN architecture. A comparative study on model performance based on accuracy, F1-score, recall, precision, number of parameters, and time was conducted, and DenseNet161 was found to demonstrate a superior performance among its counterparts. In addition, advanced optimization techniques such as normalization, mixed-up augmentation, and label smoothing were also employed on DenseNet to further refine its performance.
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Affiliation(s)
- Thinam Tamang
- Madan Bhandari Memorial College, New Baneshwor, Kathmandu 44600, Nepal
| | - Sushish Baral
- Department of Robotics and AI, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
- Correspondence: (S.B.); (M.P.P.)
| | - May Phu Paing
- Department of Biomedical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
- Correspondence: (S.B.); (M.P.P.)
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19
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Chola C, Muaad AY, Bin Heyat MB, Benifa JVB, Naji WR, Hemachandran K, Mahmoud NF, Samee NA, Al-Antari MA, Kadah YM, Kim TS. BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification. Diagnostics (Basel) 2022; 12:diagnostics12112815. [PMID: 36428875 PMCID: PMC9689932 DOI: 10.3390/diagnostics12112815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/03/2022] [Accepted: 11/12/2022] [Indexed: 11/19/2022] Open
Abstract
Blood cells carry important information that can be used to represent a person's current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet. For the purpose of establishing the dependability and viability of BCNet, exhaustive experiments consisting of five-fold cross-validation tests are carried out. Using the transfer learning strategy, we conducted in-depth comprehensive experiments on the proposed BCNet's architecture and test it with three optimizers of ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD). Meanwhile, the performance of the proposed BCNet is directly compared using the same dataset with the state-of-the-art deep learning models of DensNet, ResNet, Inception, and MobileNet. When employing the different optimizers, the BCNet framework demonstrated better classification performance with ADAM and RMSP optimizers. The best evaluation performance was achieved using the RMSP optimizer in terms of 98.51% accuracy and 96.24% F1-score. Compared with the baseline model, the BCNet clearly improved the prediction accuracy performance 1.94%, 3.33%, and 1.65% using the optimizers of ADAM, RMSP, and SGD, respectively. The proposed BCNet model outperformed the AI models of DenseNet, ResNet, Inception, and MobileNet in terms of the testing time of a single blood cell image by 10.98, 4.26, 2.03, and 0.21 msec. In comparison to the most recent deep learning models, the BCNet model could be able to generate encouraging outcomes. It is essential for the advancement of healthcare facilities to have such a recognition rate improving the detection performance of the blood cells.
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Affiliation(s)
- Channabasava Chola
- Department of Electronics and Information Convergence Engineering, College of Electronics and Information, Kyung Hee University, Suwon-si 17104, Republic of Korea
| | - Abdullah Y. Muaad
- Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
- Centre for VLSI and Embedded System Technologies, International Institute of Information Technology, Hyderabad 500032, India
- Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
| | - J. V. Bibal Benifa
- Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Kerala 686635, India
| | - Wadeea R. Naji
- Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India
| | - K. Hemachandran
- Department of Artificial Intelligence, Woxsen University, Hyderabad 502345, India
| | - Noha F. Mahmoud
- Rehabilitation Sciences Department, Health and Rehabilitation Sciences College, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
- Correspondence: (N.A.S.); (M.A.A.-A.); (Y.M.K.); (T.-S.K.)
| | - Mugahed A. Al-Antari
- Department of Artificial Intelligence, College of Software and Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
- Correspondence: (N.A.S.); (M.A.A.-A.); (Y.M.K.); (T.-S.K.)
| | - Yasser M. Kadah
- Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah 22254, Saudi Arabia
- Biomedical Engineering Department, Cairo University, Giza 12613, Egypt
- Correspondence: (N.A.S.); (M.A.A.-A.); (Y.M.K.); (T.-S.K.)
| | - Tae-Seong Kim
- Department of Electronics and Information Convergence Engineering, College of Electronics and Information, Kyung Hee University, Suwon-si 17104, Republic of Korea
- Correspondence: (N.A.S.); (M.A.A.-A.); (Y.M.K.); (T.-S.K.)
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20
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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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21
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Pałczyński K, Ledziński D, Andrysiak T. Entropy Measurements for Leukocytes' Surrounding Informativeness Evaluation for Acute Lymphoblastic Leukemia Classification. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1560. [PMID: 36359651 PMCID: PMC9689677 DOI: 10.3390/e24111560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/20/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
The study of leukemia classification using deep learning techniques has been conducted by multiple research teams worldwide. Although deep convolutional neural networks achieved high quality of sick vs. healthy patient discrimination, their inherent lack of human interpretability of the decision-making process hinders the adoption of deep learning techniques in medicine. Research involving deep learning proved that distinguishing between healthy and sick patients using microscopic images of lymphocytes is possible. However, it could not provide information on the intermediate steps in the diagnosis process. As a result, despite numerous examinations, it is still unclear whether the lymphocyte is the only object in the microscopic picture containing leukemia-related information or if the leukocyte's surroundings also contain the desired information. In this work, entropy measures and machine learning models were applied to study the informativeness of both whole images and lymphocytes' surroundings alone for Leukemia classification. This work aims to provide human-interpretable features marking the probability of sickness occurrence. The research stated that the hue distribution of images with lymphocytes obfuscated alone is informative enough to facilitate 93.0% accuracy in healthy vs. sick classification. The research was conducted on the ALL-IDB2 dataset.
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22
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Claro ML, Veras RDM, Santana AM, Vogado LHS, Braz Junior G, Medeiros FND, Tavares JMR. Assessing the impact of data augmentation and a combination of CNNs on leukemia classification. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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23
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Leveraging Deep Learning and Grab Cut for Automatic Segmentation of White Blood Cell Images. JOURNAL OF BIOMIMETICS BIOMATERIALS AND BIOMEDICAL ENGINEERING 2022. [DOI: 10.4028/p-oj4d78] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
White blood cell image segmentation provides the opportunity for medical experts to objectively diagnose the medical conditions of patients suffering from Leukemia, for example. Due to the rigorous nature of cell image acquisition (staining process and non-uniform illumination) efficient tools must be deployed to achieve the desired segmentation result. In this paper, a deep learning model is proposed together with a grab cut. The developed deep learning model provides an initial coarse segmentation of white blood cell images. However, the objective of this segmentation is to localize or identify regions of interest from white blood cell images. A bounding is generated from the localized cell image and then used to initiate an automatic cell image segmentation using grab cut. Results of the two publicly available datasets of white blood cell images are considered satisfactory on the proposed model.
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24
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Single Channel Image Enhancement (SCIE) of White Blood Cells Based on Virtual Hexagonal Filter (VHF) Designed over Square Trellis. J Pers Med 2022; 12:jpm12081232. [PMID: 36013181 PMCID: PMC9410214 DOI: 10.3390/jpm12081232] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/06/2022] [Accepted: 07/12/2022] [Indexed: 11/17/2022] Open
Abstract
White blood cells (WBCs) are the important constituent of a blood cell. These blood cells are responsible for defending the body against infections. Abnormalities identified in WBC smears lead to the diagnosis of disease types such as leukocytosis, hepatitis, and immune system disorders. Digital image analysis for infection detection at an early stage can help fast and precise diagnosis, as compared to manual inspection. Sometimes, acquired blood cell smear images from an L2-type microscope are of very low quality. The manual handling, haziness, and dark areas of the image become problematic for an efficient and accurate diagnosis. Therefore, WBC image enhancement needs attention for an effective diagnosis of the disease. This paper proposed a novel virtual hexagonal trellis (VHT)-based image filtering method for WBC image enhancement and contrast adjustment. In this method, a filter named the virtual hexagonal filter (VHF), of size 3 × 3, and based on a hexagonal structure, is formulated by using the concept of the interpolation of real and square grid pixels. This filter is convolved with WBC ALL-IBD images for enhancement and contrast adjustment. The proposed filter improves the results both visually and statically. A comparison with existing image enhancement approaches proves the validity of the proposed work.
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25
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Leukocyte Segmentation Method Based on Adaptive Retinex Correction and U-Net. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9951582. [PMID: 35832136 PMCID: PMC9273417 DOI: 10.1155/2022/9951582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 06/17/2022] [Accepted: 06/20/2022] [Indexed: 11/17/2022]
Abstract
To address the issues of uneven illumination and inconspicuous leukocyte properties in the gathered cell pictures, a leukocyte segmentation method based on adaptive retinex correction and U-net was proposed. The procedure begins by processing a peripheral blood image to clearly distinguish leukocytes from other components in the image. The adaptive retinex correction, which is based on multiscale retinex with colour replication (MSRCR), redefines the colour recovery function by introducing Michelson contrast. Then, the image is trained with the U-net convolutional neural network, and the leukocyte segmentation is completed. The innovation is in the manner of processing peripheral blood images, which improves the accuracy of leukocyte segmentation. This study conducts experimental evaluations on the Cellavision, BCCD, and LISC datasets. The experimental results show that the method in this study is better than the current best method, and the segmentation accuracy rate reaches 98.87%.
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26
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Dong YJ, Lin MQ, Fang X, Xie ZY, Luo R, Teng X, Li B, Li B, Li LZ, Jin HY, Yu QX, Lv GY, Chen SH. Modulating effects of a functional food containing Dendrobium officinale on immune response and gut microbiota in mice treated with cyclophosphamide. J Funct Foods 2022. [DOI: 10.1016/j.jff.2022.105102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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27
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Du B, Zhang X, Zhu C, Wu Y, Ji H, Zhang Y, Yue X. Immunomodulatory and Antioxidant Effects of Polysaccharides from
Pleurotus ostreatus
on immunosuppressed mice. STARCH-STARKE 2022. [DOI: 10.1002/star.202200009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Biqi Du
- College of Food Engineering and Nutrition Science Shaanxi Normal University Xi'an 710119 China
| | - Xiao Zhang
- College of Food Engineering and Nutrition Science Shaanxi Normal University Xi'an 710119 China
| | - Caiping Zhu
- College of Food Engineering and Nutrition Science Shaanxi Normal University Xi'an 710119 China
| | - Yuan Wu
- College of Food Engineering and Nutrition Science Shaanxi Normal University Xi'an 710119 China
| | - Huijie Ji
- College of Food Engineering and Nutrition Science Shaanxi Normal University Xi'an 710119 China
| | - Yifan Zhang
- College of Food Engineering and Nutrition Science Shaanxi Normal University Xi'an 710119 China
| | - Xiaoxia Yue
- College of Food Engineering and Nutrition Science Shaanxi Normal University Xi'an 710119 China
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28
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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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Leng B, Leng M, Ge M, Dong W. Knowledge distillation-based deep learning classification network for peripheral blood leukocytes. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Zhao Y, Fu C, Xu S, Cao L, Ma HF. LFANet: Lightweight feature attention network for abnormal cell segmentation in cervical cytology images. Comput Biol Med 2022; 145:105500. [PMID: 35421793 DOI: 10.1016/j.compbiomed.2022.105500] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 03/16/2022] [Accepted: 04/04/2022] [Indexed: 11/19/2022]
Abstract
With the widely applied computer-aided diagnosis techniques in cervical cancer screening, cell segmentation has become a necessary step to determine the progression of cervical cancer. Traditional manual methods alleviate the dilemma caused by the shortage of medical resources to a certain extent. Unfortunately, with their low segmentation accuracy for abnormal cells, the complex process cannot realize an automatic diagnosis. In addition, various methods on deep learning can automatically extract image features with high accuracy and small error, making artificial intelligence increasingly popular in computer-aided diagnosis. However, they are not suitable for clinical practice because those complicated models would result in more redundant parameters from networks. To address the above problems, a lightweight feature attention network (LFANet), extracting differentially abundant feature information of objects with various resolutions, is proposed in this study. The model can accurately segment both the nucleus and cytoplasm regions in cervical images. Specifically, a lightweight feature extraction module is designed as an encoder to extract abundant features of input images, combining with depth-wise separable convolution, residual connection and attention mechanism. Besides, the feature layer attention module is added to precisely recover pixel location, which employs the global high-level information as a guide for the low-level features, capturing dependencies of channel features. Finally, our LFANet model is evaluated on all four independent datasets. The experimental results demonstrate that compared with other advanced methods, our proposed network achieves state-of-the-art performance with a low computational complexity.
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Affiliation(s)
- Yanli Zhao
- School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China; School of Electrical Information Engineering, Ningxia Institute of Technology, Shizuishan, 753000, China
| | - Chong Fu
- School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110819, China; Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, China.
| | - Sen Xu
- General Hospital of Northern Theatre Command, Shenyang, 110016, China
| | - Lin Cao
- School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing, 100101, China
| | - Hong-Feng Ma
- Dopamine Group Ltd., Auckland, 1542, New Zealand
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31
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Lee BD, Gitter A, Greene CS, Raschka S, Maguire F, Titus AJ, Kessler MD, Lee AJ, Chevrette MG, Stewart PA, Britto-Borges T, Cofer EM, Yu KH, Carmona JJ, Fertig EJ, Kalinin AA, Signal B, Lengerich BJ, Triche TJ, Boca SM. Ten quick tips for deep learning in biology. PLoS Comput Biol 2022; 18:e1009803. [PMID: 35324884 PMCID: PMC8946751 DOI: 10.1371/journal.pcbi.1009803] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Affiliation(s)
- Benjamin D. Lee
- In-Q-Tel Labs, Arlington, Virginia, United States of America
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Morgridge Institute for Research, Madison, Wisconsin, United States of America
| | - Casey S. Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, United States of America
- Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Sebastian Raschka
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Finlay Maguire
- Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Alexander J. Titus
- University of New Hampshire, Manchester, New Hampshire, United States of America
- Bioeconomy.XYZ, Manchester, New Hampshire, United States of America
| | - Michael D. Kessler
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, United States of America
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Alexandra J. Lee
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Genomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Marc G. Chevrette
- Wisconsin Institute for Discovery and Department of Plant Pathology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Paul Allen Stewart
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Thiago Britto-Borges
- Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg, Heidelberg, Germany
- Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Heidelberg, Germany
| | - Evan M. Cofer
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Juan Jose Carmona
- Philips Healthcare, Cambridge, Massachusetts, United States of America
| | - Elana J. Fertig
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Biomedical Engineering, Department of Applied Mathematics and Statistics, Convergence Institute, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Alexandr A. Kalinin
- Medical Big Data Group, Shenzhen Research Institute of Big Data, Shenzhen, China
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Brandon Signal
- School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, Australia
| | - Benjamin J. Lengerich
- Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Timothy J. Triche
- Center for Epigenetics, Van Andel Research Institute, Grand Rapids, Michigan, United States of America
- Department of Pediatrics, College of Human Medicine, Michigan State University, East Lansing, Michigan, United States of America
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Simina M. Boca
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, District of Columbia, United States of America
- Department of Oncology, Georgetown University Medical Center, Washington, DC, United States of America
- Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington, DC, United States of America
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Washington, DC, United States of America
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32
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Xu F, Li X, Yang H, Wang Y, Xiang W. TE-YOLOF: Tiny and efficient YOLOF for blood cell detection. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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33
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Kouzehkanan ZM, Saghari S, Tavakoli S, Rostami P, Abaszadeh M, Mirzadeh F, Satlsar ES, Gheidishahran M, Gorgi F, Mohammadi S, Hosseini R. A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm. Sci Rep 2022; 12:1123. [PMID: 35064165 PMCID: PMC8782871 DOI: 10.1038/s41598-021-04426-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 12/10/2021] [Indexed: 12/16/2022] Open
Abstract
Accurate and early detection of anomalies in peripheral white blood cells plays a crucial role in the evaluation of well-being in individuals and the diagnosis and prognosis of hematologic diseases. For example, some blood disorders and immune system-related diseases are diagnosed by the differential count of white blood cells, which is one of the common laboratory tests. Data is one of the most important ingredients in the development and testing of many commercial and successful automatic or semi-automatic systems. To this end, this study introduces a free access dataset of normal peripheral white blood cells called Raabin-WBC containing about 40,000 images of white blood cells and color spots. For ensuring the validity of the data, a significant number of cells were labeled by two experts. Also, the ground truths of the nuclei and cytoplasm are extracted for 1145 selected cells. To provide the necessary diversity, various smears have been imaged, and two different cameras and two different microscopes were used. We did some preliminary deep learning experiments on Raabin-WBC to demonstrate how the generalization power of machine learning methods, especially deep neural networks, can be affected by the mentioned diversity. Raabin-WBC as a public data in the field of health can be used for the model development and testing in different machine learning tasks including classification, detection, segmentation, and localization.
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Affiliation(s)
- Zahra Mousavi Kouzehkanan
- School of ECE, College of Engineering, University of Tehran, Tehran, Iran.,Nimaad Health Equipment Development Company, Tehran, Iran
| | - Sepehr Saghari
- Nimaad Health Equipment Development Company, Tehran, Iran.,Graduated Bachelor of Laboratory of Sciences, Paramedical Faculty of Guilan, University of Medical of Sciences, Langarud, Gilan, Iran
| | - Sajad Tavakoli
- Nimaad Health Equipment Development Company, Tehran, Iran.,Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Peyman Rostami
- Nimaad Health Equipment Development Company, Tehran, Iran.,School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
| | | | - Farzaneh Mirzadeh
- Nimaad Health Equipment Development Company, Tehran, Iran.,School of Medicine, Tarbiat Modares University, Tehran, Iran
| | - Esmaeil Shahabi Satlsar
- Nimaad Health Equipment Development Company, Tehran, Iran.,Flow Cytometry Department, Takhte Tavous Patobiology Lab, Tehran, Iran
| | - Maryam Gheidishahran
- Department of Hematology and Blood Transfusion, School of Allied Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Fatemeh Gorgi
- Bachelor of Laboratory of Sciences, Faculty of Paramedical, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saeed Mohammadi
- Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Reshad Hosseini
- School of ECE, College of Engineering, University of Tehran, Tehran, Iran.
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Cheuque C, Querales M, León R, Salas R, Torres R. An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification. Diagnostics (Basel) 2022; 12:diagnostics12020248. [PMID: 35204339 PMCID: PMC8871319 DOI: 10.3390/diagnostics12020248] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/17/2021] [Accepted: 12/28/2021] [Indexed: 01/27/2023] Open
Abstract
The evaluation of white blood cells is essential to assess the quality of the human immune system; however, the assessment of the blood smear depends on the pathologist’s expertise. Most machine learning tools make a one-level classification for white blood cell classification. This work presents a two-stage hybrid multi-level scheme that efficiently classifies four cell groups: lymphocytes and monocytes (mononuclear) and segmented neutrophils and eosinophils (polymorphonuclear). At the first level, a Faster R-CNN network is applied for the identification of the region of interest of white blood cells, together with the separation of mononuclear cells from polymorphonuclear cells. Once separated, two parallel convolutional neural networks with the MobileNet structure are used to recognize the subclasses in the second level. The results obtained using Monte Carlo cross-validation show that the proposed model has a performance metric of around 98.4% (accuracy, recall, precision, and F1-score). The proposed model represents a good alternative for computer-aided diagnosis (CAD) tools for supporting the pathologist in the clinical laboratory in assessing white blood cells from blood smear images.
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Affiliation(s)
- César Cheuque
- Facultad de Ingeniería, Universidad Andres Bello, Viña del Mar 2531015, Chile; (C.C.); (R.L.)
| | - Marvin Querales
- Escuela de Tecnología Médica, Universidad de Valparaíso, Viña del Mar 2540064, Chile;
| | - Roberto León
- Facultad de Ingeniería, Universidad Andres Bello, Viña del Mar 2531015, Chile; (C.C.); (R.L.)
| | - Rodrigo Salas
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Escuela de Ingeniería C. Biomédica, Universidad de Valparaíso, Valparaíso 2362905, Chile;
- Instituto Milenio Intelligent Healthcare Engineering, Valparaíso 2362905, Chile
| | - Romina Torres
- Facultad de Ingeniería, Universidad Andres Bello, Viña del Mar 2531015, Chile; (C.C.); (R.L.)
- Instituto Milenio Intelligent Healthcare Engineering, Valparaíso 2362905, Chile
- Correspondence: ; Tel.: +56-32-2845315
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35
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Wittscher L, Diers J, Pigorsch C. Improving image classification robustness using self‐supervision. Stat (Int Stat Inst) 2022. [DOI: 10.1002/sta4.455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Ladyna Wittscher
- Economic and Social Statistics Friedrich‐Schiller‐University Jena Jena Germany
| | - Jan Diers
- Economic and Social Statistics Friedrich‐Schiller‐University Jena Jena Germany
| | - Christian Pigorsch
- Economic and Social Statistics Friedrich‐Schiller‐University Jena Jena Germany
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36
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37
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Wang L, Li M, Fang X, Nappi M, Wan S. Improving random walker segmentation using a nonlocal bipartite graph. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103154] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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38
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Garcia-Lamont F, Alvarado M, Cervantes J. Systematic segmentation method based on PCA of image hue features for white blood cell counting. PLoS One 2021; 16:e0261857. [PMID: 34972155 PMCID: PMC8719728 DOI: 10.1371/journal.pone.0261857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 12/10/2021] [Indexed: 11/25/2022] Open
Abstract
Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukocytes present high chromatic variance; this results in increased contrast with the average hue of the other blood smear elements. Second, chromaticity is processed as a feature, without separating hue components; this is different to most of the current automation that perform mathematical operations between hue components in an intuitive way. As a result of this systematic method, WBC recognition is computationally efficient, overlapping WBCs are separated, and the final count is more precise. In experiments with the ALL-IDB benchmark, the performance of the proposed segmentation was assessed by comparing the WBC from the processed images with the ground truth. Compared with previous methods, the proposed method achieved similar results in sensitivity and precision and approximately 0.2% higher specificity and 0.3% higher accuracy for pixel classification in the segmentation stage; as well, the counting results are similar to previous works.
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Affiliation(s)
- Farid Garcia-Lamont
- Universidad Autónoma del Estado de México, Centro Universitario UAEM Texcoco, Texcoco-Estado de México, México
| | - Matias Alvarado
- Centro de Investigación y de Estudios Avanzados del IPN, Departamento de Computación, México city, CDMX-México, México
| | - Jair Cervantes
- Universidad Autónoma del Estado de México, Centro Universitario UAEM Texcoco, Texcoco-Estado de México, México
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39
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Dong N, Zhai MD, Chang JF, Wu CH. A self-adaptive approach for white blood cell classification towards point-of-care testing. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107709] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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40
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Baby D, Devaraj SJ, Anishin Raj MM. Leukocyte classification based on statistical measures of radon transform for monitoring health condition. Biomed Phys Eng Express 2021; 7. [PMID: 34624876 DOI: 10.1088/2057-1976/ac2e16] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/08/2021] [Indexed: 11/12/2022]
Abstract
In the medical field, automated and computerised analytic tools are essential for faster disease diagnosis. The main objective of this research work is to classify the leukocytes accurately into four different subtypes based on the pattern of the nucleus. The features are extracted from the segmented nucleus, which play a vital role in the pattern recognition. The technique comprises a novel idea of computing the statistical measures such as peak difference and standard deviation of the radon transformed graph for a single angle of rotation along with other features. Three Gray Level Co-occurrence Matrix (GLCM) based features, two geometric features and four RST moment invariants are also extracted for feature fusion. The fused feature vectors are trained and evaluated using random forest classification algorithm.This method provides an overall accuracy of 97.61% and it is able to determine the lymphocyte, neutrophil and eosinophil with 100% accuracy. The classification without incorporating radon transform features is also performed which provides an accuracy of only 80.95%.
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Affiliation(s)
- Diana Baby
- Research Scholar, Department of CSE, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
| | - Sujitha Juliet Devaraj
- Department of CSE, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
| | - M M Anishin Raj
- Department of CSE, Viswajyothi College of Engineering and Technology, Kerala, India
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41
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Aletti G, Benfenati A, Naldi G. A Semiautomatic Multi-Label Color Image Segmentation Coupling Dirichlet Problem and Colour Distances. J Imaging 2021; 7:208. [PMID: 34677294 PMCID: PMC8539020 DOI: 10.3390/jimaging7100208] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/26/2021] [Accepted: 09/28/2021] [Indexed: 11/17/2022] Open
Abstract
Image segmentation is an essential but critical component in low level vision, image analysis, pattern recognition, and now in robotic systems. In addition, it is one of the most challenging tasks in image processing and determines the quality of the final results of the image analysis. Colour based segmentation could hence offer more significant extraction of information as compared to intensity or texture based segmentation. In this work, we propose a new local or global method for multi-label segmentation that combines a random walk based model with a direct label assignment computed using a suitable colour distance. Our approach is a semi-automatic image segmentation technique, since it requires user interaction for the initialisation of the segmentation process. The random walk part involves a combinatorial Dirichlet problem for a weighted graph, where the nodes are the pixel of the image, and the positive weights are related to the distances between pixels: in this work we propose a novel colour distance for computing such weights. In the random walker model we assign to each pixel of the image a probability quantifying the likelihood that the node belongs to some subregion. The computation of the colour distance is pursued by employing the coordinates in a colour space (e.g., RGB, XYZ, YCbCr) of a pixel and of the ones in its neighbourhood (e.g., in a 8-neighbourhood). The segmentation process is, therefore, reduced to an optimisation problem coupling the probabilities from the random walker approach, and the similarity with respect the labelled pixels. A further investigation involves an adaptive preprocess strategy using a regression tree for learning suitable weights to be used in the computation of the colour distance. We discuss the properties of the new method also by comparing with standard random walk and k-means approaches. The experimental results carried on the White Blood Cell (WBC) dataset and GrabCut datasets show the remarkable performance of the proposed method in comparison with state-of-the-art methods, such as normalised random walk and normalised lazy random walk, with respect to segmentation quality and computational time. Moreover, it reveals to be very robust with respect to the presence of noise and to the choice of the colourspace.
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42
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Chanti DA, Duque VG, Crouzier M, Nordez A, Lacourpaille L, Mateus D. IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric Ultrasound. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2615-2628. [PMID: 33560982 DOI: 10.1109/tmi.2021.3058303] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
We present an accurate, fast and efficient method for segmentation and muscle mask propagation in 3D freehand ultrasound data, towards accurate volume quantification. A deep Siamese 3D Encoder-Decoder network that captures the evolution of the muscle appearance and shape for contiguous slices is deployed. We use it to propagate a reference mask annotated by a clinical expert. To handle longer changes of the muscle shape over the entire volume and to provide an accurate propagation, we devise a Bidirectional Long Short Term Memory module. Also, to train our model with a minimal amount of training samples, we propose a strategy combining learning from few annotated 2D ultrasound slices with sequential pseudo-labeling of the unannotated slices. We introduce a decremental update of the objective function to guide the model convergence in the absence of large amounts of annotated data. After training with a few volumes, the decremental update strategy switches from a weak supervised training to a few-shot setting. Finally, to handle the class-imbalance between foreground and background muscle pixels, we propose a parametric Tversky loss function that learns to penalize adaptively the false positives and the false negatives. We validate our approach for the segmentation, label propagation, and volume computation of the three low-limb muscles on a dataset of 61600 images from 44 subjects. We achieve a Dice score coefficient of over 95% and a volumetric error of 1.6035 ± 0.587%.
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43
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Applying Self-Supervised Learning to Medicine: Review of the State of the Art and Medical Implementations. INFORMATICS 2021. [DOI: 10.3390/informatics8030059] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently, in medicine and healthcare, as well as in most other industries, the two most prevalent machine learning paradigms are supervised learning and transfer learning. Both practices rely on large-scale, manually annotated datasets to train increasingly complex models. However, the requirement of data to be manually labeled leaves an excess of unused, unlabeled data available in both public and private data repositories. Self-supervised learning (SSL) is a growing area of machine learning that can take advantage of unlabeled data. Contrary to other machine learning paradigms, SSL algorithms create artificial supervisory signals from unlabeled data and pretrain algorithms on these signals. The aim of this review is two-fold: firstly, we provide a formal definition of SSL, divide SSL algorithms into their four unique subsets, and review the state of the art published in each of those subsets between the years of 2014 and 2020. Second, this work surveys recent SSL algorithms published in healthcare, in order to provide medical experts with a clearer picture of how they can integrate SSL into their research, with the objective of leveraging unlabeled data.
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44
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Lee SMW, Shaw A, Simpson JL, Uminsky D, Garratt LW. Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation. Sci Rep 2021; 11:16917. [PMID: 34413367 PMCID: PMC8377024 DOI: 10.1038/s41598-021-96067-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 08/03/2021] [Indexed: 11/08/2022] Open
Abstract
Differential cell counts is a challenging task when applying computer vision algorithms to pathology. Existing approaches to train cell recognition require high availability of multi-class segmentation and/or bounding box annotations and suffer in performance when objects are tightly clustered. We present differential count network ("DCNet"), an annotation efficient modality that utilises keypoint detection to locate in brightfield images the centre points of cells (not nuclei) and their cell class. The single centre point annotation for DCNet lowered burden for experts to generate ground truth data by 77.1% compared to bounding box labeling. Yet centre point annotation still enabled high accuracy when training DCNet on a multi-class algorithm on whole cell features, matching human experts in all 5 object classes in average precision and outperforming humans in consistency. The efficacy and efficiency of the DCNet end-to-end system represents a significant progress toward an open source, fully computationally approach to differential cell count based diagnosis that can be adapted to any pathology need.
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Affiliation(s)
- Sarada M W Lee
- Perth Machine Learning Group, Perth, WA, 6000, Australia
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, 2308, Australia
| | - Andrew Shaw
- Data Institute, University of San Francisco, San Francisco, CA, 94117, USA
| | - Jodie L Simpson
- School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, 2308, Australia
- Priority Research Centre for Healthy Lungs, University of Newcastle, Callaghan, NSW, 2308, Australia
| | - David Uminsky
- Department of Computer Science, University of Chicago, Chicago, IL, 60637, USA
| | - Luke W Garratt
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Nedlands, WA, 6009, Australia.
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Vogado L, Veras R, Aires K, Araújo F, Silva R, Ponti M, Tavares JMRS. Diagnosis of Leukaemia in Blood Slides Based on a Fine-Tuned and Highly Generalisable Deep Learning Model. SENSORS (BASEL, SWITZERLAND) 2021; 21:2989. [PMID: 33923209 PMCID: PMC8123151 DOI: 10.3390/s21092989] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Revised: 04/19/2021] [Accepted: 04/21/2021] [Indexed: 02/06/2023]
Abstract
Leukaemia is a dysfunction that affects the production of white blood cells in the bone marrow. Young cells are abnormally produced, replacing normal blood cells. Consequently, the person suffers problems in transporting oxygen and in fighting infections. This article proposes a convolutional neural network (CNN) named LeukNet that was inspired on convolutional blocks of VGG-16, but with smaller dense layers. To define the LeukNet parameters, we evaluated different CNNs models and fine-tuning methods using 18 image datasets, with different resolution, contrast, colour and texture characteristics. We applied data augmentation operations to expand the training dataset, and the 5-fold cross-validation led to an accuracy of 98.61%. To evaluate the CNNs generalisation ability, we applied a cross-dataset validation technique. The obtained accuracies using cross-dataset experiments on three datasets were 97.04, 82.46 and 70.24%, which overcome the accuracies obtained by current state-of-the-art methods. We conclude that using the most common and deepest CNNs may not be the best choice for applications where the images to be classified differ from those used in pre-training. Additionally, the adopted cross-dataset validation approach proved to be an excellent choice to evaluate the generalisation capability of a model, as it considers the model performance on unseen data, which is paramount for CAD systems.
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Affiliation(s)
- Luis Vogado
- Departamento de Computação, Universidade Federal do Piauí, Teresina 64049-550, Brazil; (L.V.); (R.V.); (K.A.)
| | - Rodrigo Veras
- Departamento de Computação, Universidade Federal do Piauí, Teresina 64049-550, Brazil; (L.V.); (R.V.); (K.A.)
| | - Kelson Aires
- Departamento de Computação, Universidade Federal do Piauí, Teresina 64049-550, Brazil; (L.V.); (R.V.); (K.A.)
| | - Flávio Araújo
- Curso de Bacharelado em Sistemas de Informação, Universidade Federal do Piauí, Picos 64607-670, Brazil; (F.A.); (R.S.)
| | - Romuere Silva
- Curso de Bacharelado em Sistemas de Informação, Universidade Federal do Piauí, Picos 64607-670, Brazil; (F.A.); (R.S.)
| | - Moacir Ponti
- Instituto de Ciências Matemáticas de de Computação, Universidade de São Paulo, São Carlos 13566-590, Brazil;
| | - João Manuel R. S. Tavares
- Departamento de Engenharia Mecânica, Faculdade de Engenharia, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Universidade do Porto, 4200-465 Porto, Portugal
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M.Roy R, P.M. A. Segmentation of leukocyte by semantic segmentation model: A deep learning approach. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102385] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Lu Y, Qin X, Fan H, Lai T, Li Z. WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.107006] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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48
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Javaid I, Zhang S, Isselmou AEK, Kamhi S, Ahmad IS, Kulsum U. Brain Tumor Classification & Segmentation by Using Advanced DNN, CNN & ResNet-50 Neural Networks. INTERNATIONAL JOURNAL OF CIRCUITS, SYSTEMS AND SIGNAL PROCESSING 2020; 14:1011-1029. [DOI: 10.46300/9106.2020.14.129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
In the medical domain, brain image classification is an extremely challenging field. Medical images play a vital role in making the doctor's precise diagnosis and in the surgery process. Adopting intelligent algorithms makes it feasible to detect the lesions of medical images quickly, and it is especially necessary to extract features from medical images. Several studies have integrated multiple algorithms toward medical images domain. Concerning feature extraction from the medical image, a vast amount of data is analyzed to achieve processing results, helping physicians deliver more precise case diagnoses. Image processing mechanism becomes extensive usage in medical science to advance the early detection and treatment aspects. In this aspect, this paper takes tumor, and healthy images as the research object and primarily performs image processing and data augmentation process to feed the dataset to the neural networks. Deep neural networks (DNN), to date, have shown outstanding achievement in classification and segmentation tasks. Carrying this concept into consideration, in this study, we adopted a pre-trained model Resnet_50 for image analysis. The paper proposed three diverse neural networks, particularly DNN, CNN, and ResNet-50. Finally, the splitting dataset is individually assigned to each simplified neural network. Once the image is classified as a tumor accurately, the OTSU segmentation is employed to extract the tumor alone. It can be examined from the experimental outcomes that the ResNet-50 algorithm shows high accuracy 0.996, precision 1.00 with best F1 score 1.0, and minimum test losses of 0.0269 in terms of Brain tumor classification. Extensive experiments prove our offered tumor detection segmentation efficiency and accuracy. To this end, our approach is comprehensive sufficient and only requires minimum pre-and post-processing, which allows its adoption in various medical image classification & segmentation tasks.
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Affiliation(s)
- Imran Javaid
- Hebei University of Technology, 8 Dingzigu 1stRd, Hongqiao Qu,China
| | - Shuai Zhang
- Hebei University of Technology, 8 Dingzigu 1stRd, Hongqiao Qu,China
| | | | - Souha Kamhi
- Hebei University of Technology, 8 Dingzigu 1stRd, Hongqiao Qu,China
| | - Isah Salim Ahmad
- Hebei University of Technology, 8 Dingzigu 1stRd, Hongqiao Qu,China
| | - Ummay Kulsum
- Hebei University of Technology, 8 Dingzigu 1stRd, Hongqiao Qu,China
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Localization and recognition of leukocytes in peripheral blood: A deep learning approach. Comput Biol Med 2020; 126:104034. [DOI: 10.1016/j.compbiomed.2020.104034] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 10/02/2020] [Accepted: 10/02/2020] [Indexed: 01/26/2023]
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50
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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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