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Saikia R, Deka R, Sarma A, Devi SS. BSNEU-net: Block Feature Map Distortion and Switchable Normalization-Based Enhanced Union-net for Acute Leukemia Detection on Heterogeneous Dataset. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01252-1. [PMID: 39322814 DOI: 10.1007/s10278-024-01252-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 08/26/2024] [Accepted: 08/27/2024] [Indexed: 09/27/2024]
Abstract
Acute leukemia is characterized by the swift proliferation of immature white blood cells (WBC) in the blood and bone marrow. It is categorized into acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), depending on whether the cell-line origin is lymphoid or myeloid, respectively. Deep learning (DL) and artificial intelligence (AI) are revolutionizing medical sciences by assisting clinicians with rapid illness identification, reducing workload, and enhancing diagnostic accuracy. This paper proposes a DL-based novel BSNEU-net framework to detect acute leukemia. It comprises 4 Union Blocks (UB) and incorporates block feature map distortion (BFMD) with switchable normalization (SN) in each UB. The UB employs union convolution to extract more discriminant features. The BFMD is adapted to acquire more generalized patterns to minimize overfitting, whereas SN layers are appended to improve the model's convergence and generalization capabilities. The uniform utilization of batch normalization across convolution layers is sensitive to the mini-batch dimension changes, which is effectively remedied by incorporating an SN layer. Here, a new dataset comprising 2400 blood smear images of ALL, AML, and healthy cases is proposed, as DL methodologies necessitate a sizeable and well-annotated dataset to combat overfitting issues. Further, a heterogeneous dataset comprising 2700 smear images is created by combining four publicly accessible benchmark datasets of ALL, AML, and healthy cases. The BSNEU-net model achieved excellent performance with 99.37% accuracy on the novel dataset and 99.44% accuracy on the heterogeneous dataset. The comparative analysis signifies the superiority of the proposed methodology with comparing schemes.
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Affiliation(s)
- Rabul Saikia
- Department of Electronics and Communication Engineering, National Institute of Technology Meghalaya, Shillong, India.
| | - Roopam Deka
- Department of Pathology & Lab Medicine, All India Institute of Medical Sciences Guwahati, Guwahati, India
| | - Anupam Sarma
- Department of Onco-Pathology, Dr. Bhubaneswar Borooah Cancer Institute, Guwahati, India
| | - Salam Shuleenda Devi
- Department of Electronics and Communication Engineering, National Institute of Technology Meghalaya, Shillong, India
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Asghar R, Kumar S, Shaukat A, Hynds P. Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review. PLoS One 2024; 19:e0292026. [PMID: 38885231 PMCID: PMC11182552 DOI: 10.1371/journal.pone.0292026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 05/13/2024] [Indexed: 06/20/2024] Open
Abstract
Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs. Following development and implementation of a formalized review protocol, a cohort of 136 primary studies published between January 2006 and May 2023 were identified from the global literature, with the most widely used techniques and best-performing WBC classification methods subsequently ascertained. Studies derived from 26 countries, with highest numbers from high-income countries including the United States (n = 32) and The Netherlands (n = 26). While WBC classification was originally rooted in conventional ML, there has been a notable shift toward the use of DL, and particularly convolutional neural networks (CNN), with 54.4% of identified studies (n = 74) including the use of CNNs, and particularly in concurrence with larger datasets and bespoke features e.g., parallel data pre-processing, feature selection, and extraction. While some conventional ML models achieved up to 99% accuracy, accuracy was shown to decrease in concurrence with decreasing dataset size. Deep learning models exhibited improved performance for more extensive datasets and exhibited higher levels of accuracy in concurrence with increasingly large datasets. Availability of appropriate datasets remains a primary challenge, potentially resolvable using data augmentation techniques. Moreover, medical training of computer science researchers is recommended to improve current understanding of leucocyte structure and subsequent selection of appropriate classification models. Likewise, it is critical that future health professionals be made aware of the power, efficacy, precision and applicability of computer science, soft computing and artificial intelligence contributions to medicine, and particularly in areas like medical imaging.
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Affiliation(s)
- Rabia Asghar
- Spatiotemporal Environmental Epidemiology Research (STEER) Group, Technological University Dublin, Dublin, Ireland
| | - Sanjay Kumar
- National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Arslan Shaukat
- National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Paul Hynds
- Spatiotemporal Environmental Epidemiology Research (STEER) Group, Technological University Dublin, Dublin, Ireland
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Shahzad M, Ali F, Shirazi SH, Rasheed A, Ahmad A, Shah B, Kwak D. Blood cell image segmentation and classification: a systematic review. PeerJ Comput Sci 2024; 10:e1813. [PMID: 38435563 PMCID: PMC10909159 DOI: 10.7717/peerj-cs.1813] [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: 09/18/2023] [Accepted: 12/18/2023] [Indexed: 03/05/2024]
Abstract
Background Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc. Methodology This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs. Results Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively. Conclusion The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.
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Affiliation(s)
- Muhammad Shahzad
- Department of Computer Science and Information Technology, Hazara University, Mansehra, Pakistan
| | - Farman Ali
- Department of Computer Science and Engineering, School of Convergence, Sungkyunkwan University, Seoul, South Korea
| | - Syed Hamad Shirazi
- Department of Computer Science and Information Technology, Hazara University, Mansehra, Pakistan
| | - Assad Rasheed
- Department of Computer Science and Information Technology, Hazara University, Mansehra, Pakistan
| | - Awais Ahmad
- Centre for Excellence in Information Technology, Institute of Management Sciences, Peshawar, Pakistan
| | - Babar Shah
- College of Technological Innovation, Zayed University, Dubai, United Arab Emirates
| | - Daehan Kwak
- Department of Computer Science and Technology, Kean University, Union, NJ, United States
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Abhishek A, Jha RK, Sinha R, Jha K. Automated detection and classification of leukemia on a subject-independent test dataset using deep transfer learning supported by Grad-CAM visualization. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
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Yao X, Sun K, Bu X, Zhao C, Jin Y. Classification of white blood cells using weighted optimized deformable convolutional neural networks. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2021; 49:147-155. [PMID: 33533656 DOI: 10.1080/21691401.2021.1879823] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 01/17/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND Machine learning (ML) algorithms have been widely used in the classification of white blood cells (WBCs). However, the performance of ML algorithms still needs to be addressed for being short of gold standard data sets, and even the implementation of the proposed algorithms. METHODS In this study, the method of two-module weighted optimized deformable convolutional neural networks (TWO-DCNN) was proposed for WBC classification. Our algorithm is characterized as two-module transfer learning and deformable convolutional (DC) layers for the betterment of robustness. To validate the performance, our method was compared with classical MLs of VGG16, VGG19, Inception-V3, ResNet-50, support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT) and random forest (RF) on our undisclosed WBC data set and public BCCD data set. RESULTS TWO-DCNN achieved the best performance with the precisions (PREs) of 95.7%, 94.5% and 91.6%, recalls (RECs) of 95.7%, 94.5% and 91.6%, F1-scores (F1s) of 95.7%, 94.5% and 91.6%, area under curves (AUCs) of 0.98, 0.97 and 0.95 for low-resolution and noisy undisclosed data sets, BCCD data set, respectively. CONCLUSIONS With accurate feature extraction and optimized network weights, the proposed TWO-DCNN showed the best performance in WBC classification for low-resolution and noisy data sets. It could be used as an alternative method for clinical applications.
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Affiliation(s)
- Xufeng Yao
- College of Medical Imaging, Jiading District Central Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Kai Sun
- College of Medical Imaging, Jiading District Central Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xixi Bu
- College of Medical Imaging, Jiading District Central Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Congyi Zhao
- College of Medical Imaging, Jiading District Central Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yu Jin
- College of Medical Imaging, Jiading District Central Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
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Qiao Y, Zhang Y, Liu N, Chen P, Liu Y. An End-to-End Pipeline for Early Diagnosis of Acute Promyelocytic Leukemia Based on a Compact CNN Model. Diagnostics (Basel) 2021; 11:diagnostics11071237. [PMID: 34359320 PMCID: PMC8304210 DOI: 10.3390/diagnostics11071237] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/06/2021] [Accepted: 07/06/2021] [Indexed: 01/31/2023] Open
Abstract
Timely microscopy screening of peripheral blood smears is essential for the diagnosis of acute promyelocytic leukemia (APL) due to the occurrence of early death (ED) before or during the initial therapy. Screening manually is time-consuming and tedious, and may lead to missed diagnosis or misdiagnosis because of subjective bias. To address these problems, we develop a three-step pipeline to help in the early diagnosis of APL from peripheral blood smears. The entire pipeline consists of leukocytes focusing, cell classification and diagnostic opinions. As the key component of the pipeline, a compact classification model based on attention embedded convolutional neural network blocks is proposed to distinguish promyelocytes from normal leukocytes. The compact classification model is validated on both the combination of two public datasets, APL-Cytomorphology_LMU and APL-Cytomorphology_JHH, as well as the clinical dataset, to yield a precision of 96.53% and 99.20%, respectively. The results indicate that our model outperforms the other evaluated popular classification models owing to its better accuracy and smaller size. Furthermore, the entire pipeline is validated on realistic patient data. The proposed method promises to act as an assistant tool for APL diagnosis.
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Affiliation(s)
- Yifan Qiao
- The College of Computer Science, Sichuan University, Chengdu 610065, China; (Y.Q.); (Y.Z.)
| | - Yi Zhang
- The College of Computer Science, Sichuan University, Chengdu 610065, China; (Y.Q.); (Y.Z.)
| | - Nian Liu
- The College of Electrical Engineering, Sichuan University, Chengdu 610065, China;
| | - Pu Chen
- The Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
- Correspondence: (P.C.); (Y.L.); Tel.: +86-021-64041990 (ext. 2435) (P.C.); +86-028-85120790 (Y.L.)
| | - Yan Liu
- The College of Electrical Engineering, Sichuan University, Chengdu 610065, China;
- Correspondence: (P.C.); (Y.L.); Tel.: +86-021-64041990 (ext. 2435) (P.C.); +86-028-85120790 (Y.L.)
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IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:6648574. [PMID: 33343851 PMCID: PMC7732373 DOI: 10.1155/2020/6648574] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 11/02/2020] [Accepted: 11/21/2020] [Indexed: 12/13/2022]
Abstract
For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients' lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.
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Li G, Mahmoudi MR, Qasem SN, Tuan BA, Pho KH. Cluster ensemble of valid small clusters. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-191530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Guang Li
- Institute of Data Science, City University of Macau, Macau
| | - Mohammad Reza Mahmoudi
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
- Department of Statistics, Faculty of Science, Fasa University, Fasa, Iran
| | - Sultan Noman Qasem
- Department of Computer Science, College of Computer and Information Sciences, AI Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
- Department of Computer Science, Faculty of Applied Science, Taiz University, Taiz, Yemen
| | - Bui Anh Tuan
- Department of Mathematics Education, Teachers College, Can Tho University, Can Tho City, Vietnam
| | - Kim-Hung Pho
- Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Abstract
Clustering ensemble indicates to an approach in which a number of (usually weak) base clusterings are performed and their consensus clustering is used as the final clustering. Knowing democratic decisions are better than dictatorial decisions, it seems clear and simple that ensemble (here, clustering ensemble) decisions are better than simple model (here, clustering) decisions. But it is not guaranteed that every ensemble is better than a simple model. An ensemble is considered to be a better ensemble if their members are valid or high-quality and if they participate according to their qualities in constructing consensus clustering. In this paper, we propose a clustering ensemble framework that uses a simple clustering algorithm based on kmedoids clustering algorithm. Our simple clustering algorithm guarantees that the discovered clusters are valid. From another point, it is also guaranteed that our clustering ensemble framework uses a mechanism to make use of each discovered cluster according to its quality. To do this mechanism an auxiliary ensemble named reference set is created by running several kmeans clustering algorithms.
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