1
|
Xia B, Innab N, Kandasamy V, Ahmadian A, Ferrara M. Intelligent cardiovascular disease diagnosis using deep learning enhanced neural network with ant colony optimization. Sci Rep 2024; 14:21777. [PMID: 39294203 PMCID: PMC11411078 DOI: 10.1038/s41598-024-71932-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: 02/08/2024] [Accepted: 09/02/2024] [Indexed: 09/20/2024] Open
Abstract
To identify patterns in big medical datasets and use Deep Learning and Machine Learning (ML) to reliably diagnose Cardio Vascular Disease (CVD), researchers are currently delving deeply into these fields. Training on large datasets and producing highly accurate validation results is exceedingly difficult. Furthermore, early and precise diagnosis is necessary due to the increased global prevalence of cardiovascular disease (CVD). However, the increasing complexity of healthcare datasets makes it challenging to detect feature connections and produce precise predictions. To address these issues, the Intelligent Cardiovascular Disease Diagnosis based on Ant Colony Optimisation with Enhanced Deep Learning (ICVD-ACOEDL) model was developed. This model employs feature selection (FS) and hyperparameter optimization to diagnose CVD. Applying a min-max scaler, medical data is first consistently prepared. The key feature that sets ICVD-ACOEDL apart is the use of Ant Colony Optimisation (ACO) to select an optimal feature subset, which in turn helps to upgrade the performance of the ensuring deep learning enhanced neural network (DLENN) classifier. The model reforms the hyperparameters of DLENN for CVD classification using Bayesian optimization. Comprehensive evaluations on benchmark medical datasets show that ICVD-ACOEDL exceeds existing techniques, indicating that it could have a significant impact on CVD diagnosis. The model furnishes a workable way to increase CVD classification efficiency and accuracy in real-world medical situations by incorporating ACO for feature selection, min-max scaling for data pre-processing, and Bayesian optimization for hyperparameter tweaking.
Collapse
Affiliation(s)
- Biao Xia
- Medical Equipment Department, Changzhou No2 Hospital Nanjing Medical University, Changzhou, 213164, Jiangsu, China.
| | - Nisreen Innab
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, 13713, Diriyah, Riyadh, Saudi Arabia
| | - Venkatachalam Kandasamy
- Department of Mathematics, Faculty of Science, University of Hradec Kralove, Hradec Kralove, Czech Republic
| | - Ali Ahmadian
- Faculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul, Turkey
| | | |
Collapse
|
2
|
Raghaw CS, Sharma A, Bansal S, Rehman MZU, Kumar N. CoTCoNet: An optimized coupled transformer-convolutional network with an adaptive graph reconstruction for leukemia detection. Comput Biol Med 2024; 179:108821. [PMID: 38972153 DOI: 10.1016/j.compbiomed.2024.108821] [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: 11/21/2023] [Revised: 06/25/2024] [Accepted: 06/25/2024] [Indexed: 07/09/2024]
Abstract
BACKGROUND Swift and accurate blood smear analyses are crucial for diagnosing leukemia and other hematological malignancies. However, manual leukocyte count and morphological evaluation remain time-consuming and prone to errors. Additionally, conventional image processing methods struggle to differentiate cells due to visual similarities between malignant and benign cell morphology. METHOD In response to above challenges, we propose Coupled Transformer Convolutional Network (CoTCoNet) framework for leukemia classification. CoTCoNet integrates dual-feature extraction to capture long-range global features and fine-grained spatial patterns, facilitating the identification of complex hematological characteristics. Additionally, the framework employs a graph-based module to uncover hidden, biologically relevant features of leukocyte cells, along with a Population-based Meta-Heuristic Algorithm for feature selection and optimization. Furthermore, we introduce a novel combination of leukocyte segmentation and synthesis, which isolates relevant regions while augmenting the training dataset with realistic leukocyte samples. This strategy isolates relevant regions while augmenting the training data with realistic leukocyte samples, enhancing feature extraction, and addressing data scarcity without compromising data integrity. RESULTS We evaluated CoTCoNet on a dataset of 16,982 annotated cells, achieving an accuracy of 0.9894 and an F1-Score of 0.9893. We tested CoTCoNet on four diverse, publicly available datasets (including those above) to assess generalizability. Results demonstrate a significant performance improvement over existing state-of-the-art approaches. CONCLUSIONS CoTCoNet represents a significant advancement in leukemia classification, offering enhanced accuracy and efficiency compared to traditional methods. By incorporating explainable visualizations that closely align with cell annotations, the framework provides deeper insights into its decision-making process, further solidifying its potential in clinical settings.
Collapse
Affiliation(s)
- Chandravardhan Singh Raghaw
- Department of Computer Science and Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, Madhya Pradesh, India.
| | - Arnav Sharma
- Department of Computer Science, The University of Texas at Dallas, 800 W Campbell Rd, Richardson, 75080, TX, USA.
| | - Shubhi Bansal
- Department of Computer Science and Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, Madhya Pradesh, India.
| | - Mohammad Zia Ur Rehman
- Department of Computer Science and Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, Madhya Pradesh, India.
| | - Nagendra Kumar
- Department of Computer Science and Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, Madhya Pradesh, India.
| |
Collapse
|
3
|
Cheng W, Liu J, Wang C, Jiang R, Jiang M, Kong F. Application of image recognition technology in pathological diagnosis of blood smears. Clin Exp Med 2024; 24:181. [PMID: 39105953 PMCID: PMC11303489 DOI: 10.1007/s10238-024-01379-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Accepted: 05/13/2024] [Indexed: 08/07/2024]
Abstract
Traditional manual blood smear diagnosis methods are time-consuming and prone to errors, often relying heavily on the experience of clinical laboratory analysts for accuracy. As breakthroughs in key technologies such as neural networks and deep learning continue to drive digital transformation in the medical field, image recognition technology is increasingly being leveraged to enhance existing medical processes. In recent years, advancements in computer technology have led to improved efficiency in the identification of blood cells in blood smears through the use of image recognition technology. This paper provides a comprehensive summary of the methods and steps involved in utilizing image recognition algorithms for diagnosing diseases in blood smears, with a focus on malaria and leukemia. Furthermore, it offers a forward-looking research direction for the development of a comprehensive blood cell pathological detection system.
Collapse
Affiliation(s)
- Wangxinjun Cheng
- Center of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China
- Queen Mary College, Nanchang University, Nanchang, 330006, China
| | - Jingshuang Liu
- Center of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China
- Queen Mary College, Nanchang University, Nanchang, 330006, China
| | - Chaofeng Wang
- Center of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China
- Queen Mary College, Nanchang University, Nanchang, 330006, China
| | - Ruiyin Jiang
- Queen Mary College, Nanchang University, Nanchang, 330006, China
| | - Mei Jiang
- Department of Clinical Laboratory, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
| | - Fancong Kong
- Center of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
| |
Collapse
|
4
|
Fan BE, Yong BSJ, Li R, Wang SSY, Aw MYN, Chia MF, Chen DTY, Neo YS, Occhipinti B, Ling RR, Ramanathan K, Ong YX, Lim KGE, Wong WYK, Lim SP, Latiff STBA, Shanmugam H, Wong MS, Ponnudurai K, Winkler S. From microscope to micropixels: A rapid review of artificial intelligence for the peripheral blood film. Blood Rev 2024; 64:101144. [PMID: 38016837 DOI: 10.1016/j.blre.2023.101144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/12/2023] [Accepted: 11/14/2023] [Indexed: 11/30/2023]
Abstract
Artificial intelligence (AI) and its application in classification of blood cells in the peripheral blood film is an evolving field in haematology. We performed a rapid review of the literature on AI and peripheral blood films, evaluating the condition studied, image datasets, machine learning models, training set size, testing set size and accuracy. A total of 283 studies were identified, encompassing 6 broad domains: malaria (n = 95), leukemia (n = 81), leukocytes (n = 72), mixed (n = 25), erythrocytes (n = 15) or Myelodysplastic syndrome (MDS) (n = 1). These publications have demonstrated high self-reported mean accuracy rates across various studies (95.5% for malaria, 96.0% for leukemia, 94.4% for leukocytes, 95.2% for mixed studies and 91.2% for erythrocytes), with an overall mean accuracy of 95.1%. Despite the high accuracy, the challenges toward real world translational usage of these AI trained models include the need for well-validated multicentre data, data standardisation, and studies on less common cell types and non-malarial blood-borne parasites.
Collapse
Affiliation(s)
- Bingwen Eugene Fan
- Department of Haematology, Tan Tock Seng Hospital, Singapore; Department of Laboratory Medicine, Khoo Teck Puat Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Bryan Song Jun Yong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Ruiqi Li
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | | | | | - Ming Fang Chia
- Department of Haematology, Tan Tock Seng Hospital, Singapore
| | | | - Yuan Shan Neo
- ASUS Intelligent Cloud Services, Singapore, Singapore
| | | | - Ryan Ruiyang Ling
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kollengode Ramanathan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Cardiothoracic Intensive Care Unit, National University Heart Centre, National University Hospital, Singapore, Singapore
| | - Yi Xiong Ong
- Department of Laboratory Medicine, Tan Tock Seng Hospital, Singapore
| | | | | | - Shu Ping Lim
- Department of Laboratory Medicine, Tan Tock Seng Hospital, Singapore
| | | | | | - Moh Sim Wong
- Department of Laboratory Medicine, Khoo Teck Puat Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Kuperan Ponnudurai
- Department of Haematology, Tan Tock Seng Hospital, Singapore; Department of Laboratory Medicine, Khoo Teck Puat Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Stefan Winkler
- ASUS Intelligent Cloud Services, Singapore, Singapore; School of Computing, National University of Singapore, Singapore
| |
Collapse
|
5
|
Park S, Park YH, Huh J, Baik SM, Park DJ. Deep learning model for differentiating acute myeloid and lymphoblastic leukemia in peripheral blood cell images via myeloblast and lymphoblast classification. Digit Health 2024; 10:20552076241258079. [PMID: 38812848 PMCID: PMC11135107 DOI: 10.1177/20552076241258079] [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: 11/06/2023] [Accepted: 05/13/2024] [Indexed: 05/31/2024] Open
Abstract
Objective Acute leukemia (AL) is a life-threatening malignant disease that occurs in the bone marrow and blood, and is classified as either acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL). Diagnosing AL warrants testing methods, such as flow cytometry, which require trained professionals, time, and money. We aimed to develop a model that can classify peripheral blood images of 12 cell types, including pathological cells associated with AL, using artificial intelligence. Methods We acquired 42,386 single-cell images of peripheral blood slides from 282 patients (82 with AML, 40 with ALL, and 160 with immature granulocytes). Results The performance of EfficientNet-V2 (B2) using the original image size exhibited the greatest accuracy (accuracy, 0.8779; precision, 0.7221; recall, 0.7225; and F1 score, 0.7210). The next-best accuracy was achieved by EfficientNet-V1 (B1), with a 256 × 256 pixels image. F1 score was the greatest for EfficientNet-V1 (B1) with the original image size. EfficientNet-V1 (B1) and EfficientNet-V2 (B2) were used to develop an ensemble model, and the accuracy (0.8858) and F1 score (0.7361) were improved. The classification performance of the developed ensemble model for the 12 cell types was good, with an area under the receiver operating characteristic curve above 0.9, and F1 scores for myeloblasts and lymphoblasts of 0.8873 and 0.8006, respectively. Conclusions The performance of the developed ensemble model for the 12 cell classifications was satisfactory, particularly for myeloblasts and lymphoblasts. We believe that the application of our model will benefit healthcare settings where the rapid and accurate diagnosis of AL is difficult.
Collapse
Affiliation(s)
- Sholhui Park
- Department of Laboratory Medicine, Ewha Womans University College of Medicine, Seoul, Korea
| | - Young Hoon Park
- Division of Hematology-Oncology, Department of Internal Medicine, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | - Jungwon Huh
- Department of Laboratory Medicine, Ewha Womans University College of Medicine, Seoul, Korea
| | - Seung Min Baik
- Division of Critical Care Medicine, Department of Surgery, College of Medicine, Ewha Womans University, Seoul, Korea
| | - Dong Jin Park
- Department of Laboratory Medicine, Eunpyeong St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| |
Collapse
|
6
|
Elsayed B, Elhadary M, Elshoeibi RM, Elshoeibi AM, Badr A, Metwally O, ElSherif RA, Salem ME, Khadadah F, Alshurafa A, Mudawi D, Yassin M. Deep learning enhances acute lymphoblastic leukemia diagnosis and classification using bone marrow images. Front Oncol 2023; 13:1330977. [PMID: 38125946 PMCID: PMC10731043 DOI: 10.3389/fonc.2023.1330977] [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: 10/31/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
Acute lymphoblastic leukemia (ALL) poses a significant health challenge, particularly in pediatric cases, requiring precise and rapid diagnostic approaches. This comprehensive review explores the transformative capacity of deep learning (DL) in enhancing ALL diagnosis and classification, focusing on bone marrow image analysis. Examining ten studies conducted between 2013 and 2023 across various countries, including India, China, KSA, and Mexico, the synthesis underscores the adaptability and proficiency of DL methodologies in detecting leukemia. Innovative DL models, notably Convolutional Neural Networks (CNNs) with Cat-Boosting, XG-Boosting, and Transfer Learning techniques, demonstrate notable approaches. Some models achieve outstanding accuracy, with one CNN reaching 100% in cancer cell classification. The incorporation of novel algorithms like Cat-Swarm Optimization and specialized CNN architectures contributes to superior classification accuracy. Performance metrics highlight these achievements, with models consistently outperforming traditional diagnostic methods. For instance, a CNN with Cat-Boosting attains 100% accuracy, while others hover around 99%, showcasing DL models' robustness in ALL diagnosis. Despite acknowledged challenges, such as the need for larger and more diverse datasets, these findings underscore DL's transformative potential in reshaping leukemia diagnostics. The high numerical accuracies accentuate a promising trajectory toward more efficient and accurate ALL diagnosis in clinical settings, prompting ongoing research to address challenges and refine DL models for optimal clinical integration.
Collapse
Affiliation(s)
| | | | | | | | - Ahmed Badr
- College of Medicine, Qatar University, Doha, Qatar
| | | | | | | | - Fatima Khadadah
- Cancer Genetics Lab, Kuwait Cancer Control Centre, Kuwait City, Kuwait
| | - Awni Alshurafa
- Department of Medical Oncology, National Center for Cancer Care and Research, Doha, Qatar
| | - Deena Mudawi
- Department of Medical Oncology, National Center for Cancer Care and Research, Doha, Qatar
| | - Mohamed Yassin
- College of Medicine, Qatar University, Doha, Qatar
- Department of Medical Oncology, National Center for Cancer Care and Research, Doha, Qatar
| |
Collapse
|
7
|
Kaur M, AlZubi AA, Jain A, Singh D, Yadav V, Alkhayyat A. DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images. Diagnostics (Basel) 2023; 13:2752. [PMID: 37685290 PMCID: PMC10486457 DOI: 10.3390/diagnostics13172752] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 09/10/2023] Open
Abstract
Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To address this challenge, researchers have turned to advanced technologies such as deep learning (DL) models. These models leverage the power of artificial intelligence to analyze complex patterns and features in medical images and data, enabling faster and more accurate diagnosis of ALL. However, the existing DL-based ALL diagnosis suffers from various challenges, such as computational complexity, sensitivity to hyperparameters, and difficulties with noisy or low-quality input images. To address these issues, in this paper, we propose a novel Deep Skip Connections-Based Dense Network (DSCNet) tailored for ALL diagnosis using peripheral blood smear images. The DSCNet architecture integrates skip connections, custom image filtering, Kullback-Leibler (KL) divergence loss, and dropout regularization to enhance its performance and generalization abilities. DSCNet leverages skip connections to address the vanishing gradient problem and capture long-range dependencies, while custom image filtering enhances relevant features in the input data. KL divergence loss serves as the optimization objective, enabling accurate predictions. Dropout regularization is employed to prevent overfitting during training, promoting robust feature representations. The experiments conducted on an augmented dataset for ALL highlight the effectiveness of DSCNet. The proposed DSCNet outperforms competing methods, showcasing significant enhancements in accuracy, sensitivity, specificity, F-score, and area under the curve (AUC), achieving increases of 1.25%, 1.32%, 1.12%, 1.24%, and 1.23%, respectively. The proposed approach demonstrates the potential of DSCNet as an effective tool for early and accurate ALL diagnosis, with potential applications in clinical settings to improve patient outcomes and advance leukemia detection research.
Collapse
Affiliation(s)
- Manjit Kaur
- School of Computer Science and Artificial Intelligence, SR University, Warangal 506371, India;
| | - Ahmad Ali AlZubi
- Department of Computer Science, Community College, King Saud University, Riyadh 11421, Saudi Arabia;
| | - Arpit Jain
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada 522302, India;
| | - Dilbag Singh
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Research and Development Cell, Lovely Professional University, Phagwara 144411, India
| | - Vaishali Yadav
- School of Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, India
| | - Ahmed Alkhayyat
- College of Technical Engineering, The Islamic University, Najaf 7003, Iraq;
| |
Collapse
|
8
|
Lv Z, Cao X, Jin X, Xu S, Deng H. High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system. Sci Rep 2023; 13:13364. [PMID: 37591969 PMCID: PMC10435561 DOI: 10.1038/s41598-023-40424-x] [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: 02/14/2023] [Accepted: 08/10/2023] [Indexed: 08/19/2023] Open
Abstract
Accurate identification and classification of bone marrow (BM) nucleated cell morphology are crucial for the diagnosis of hematological diseases. However, the subjective and time-consuming nature of manual identification by pathologists hinders prompt diagnosis and patient treatment. To address this issue, we developed Morphogo, a convolutional neural network-based system for morphological examination. Morphogo was trained using a vast dataset of over 2.8 million BM nucleated cell images. Its performance was evaluated using 508 BM cases that were categorized into five groups based on the degree of morphological abnormalities, comprising a total of 385,207 BM nucleated cells. The results demonstrated Morphogo's ability to identify over 25 different types of BM nucleated cells, achieving a sensitivity of 80.95%, specificity of 99.48%, positive predictive value of 76.49%, negative predictive value of 99.44%, and an overall accuracy of 99.01%. In most groups, Morphogo cell analysis and Pathologists' proofreading showed high intragroup correlation coefficients for granulocytes, erythrocytes, lymphocytes, monocytes, and plasma cells. These findings further validate the practical applicability of the Morphogo system in clinical practice and emphasize its value in assisting pathologists in diagnosing blood disorders.
Collapse
Affiliation(s)
- Zhanwu Lv
- Bone Marrow Chamber, Guangzhou Kingmed Diagnostic Laboratory Group Co., Ltd., Guangzhou, 510330, China.
| | - Xinyi Cao
- Division of Medical Technology Development, Hangzhou Zhiwei Information Technology Co., Ltd., Hangzhou, 310000, China
| | - Xinyi Jin
- Division of Medical Technology Development, Hangzhou Zhiwei Information Technology Co., Ltd., Hangzhou, 310000, China
| | - Shuangqing Xu
- Bone Marrow Chamber, Guangzhou Kingmed Diagnostic Laboratory Group Co., Ltd., Guangzhou, 510330, China
| | - Huangling Deng
- Bone Marrow Chamber, Guangzhou Kingmed Diagnostic Laboratory Group Co., Ltd., Guangzhou, 510330, China
| |
Collapse
|
9
|
Atteia G, Alnashwan R, Hassan M. Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification. Diagnostics (Basel) 2023; 13:2672. [PMID: 37627931 PMCID: PMC10453878 DOI: 10.3390/diagnostics13162672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Acute lymphoblastic leukemia (ALL) is a lethal blood cancer that is characterized by an abnormal increased number of immature lymphocytes in the blood or bone marrow. For effective treatment of ALL, early assessment of the disease is essential. Manual examination of stained blood smear images is current practice for initially screening ALL. This practice is time-consuming and error-prone. In order to effectively diagnose ALL, numerous deep-learning-based computer vision systems have been developed for detecting ALL in blood peripheral images (BPIs). Such systems extract a huge number of image features and use them to perform the classification task. The extracted features may contain irrelevant or redundant features that could reduce classification accuracy and increase the running time of the classifier. Feature selection is considered an effective tool to mitigate the curse of the dimensionality problem and alleviate its corresponding shortcomings. One of the most effective dimensionality-reduction tools is principal component analysis (PCA), which maps input features into an orthogonal space and extracts the features that convey the highest variability from the data. Other feature selection approaches utilize evolutionary computation (EC) to search the feature space and localize optimal features. To profit from both feature selection approaches in improving the classification performance of ALL, in this study, a new hybrid deep-learning-based feature engineering approach is proposed. The introduced approach integrates the powerful capability of PCA and particle swarm optimization (PSO) approaches in selecting informative features from BPI mages with the power of pre-trained CNNs of feature extraction. Image features are first extracted through the feature-transfer capability of the GoogleNet convolutional neural network (CNN). PCA is utilized to generate a feature set of the principal components that covers 95% of the variability in the data. In parallel, bio-inspired particle swarm optimization is used to search for the optimal image features. The PCA and PSO-derived feature sets are then integrated to develop a hybrid set of features that are then used to train a Bayesian-based optimized support vector machine (SVM) and subspace discriminant ensemble-learning (SDEL) classifiers. The obtained results show improved classification performance for the ML classifiers trained by the proposed hybrid feature set over the original PCA, PSO, and all extracted feature sets for ALL multi-class classification. The Bayesian-optimized SVM trained with the proposed hybrid PCA-PSO feature set achieves the highest classification accuracy of 97.4%. The classification performance of the proposed feature engineering approach competes with the state of the art.
Collapse
Affiliation(s)
- Ghada Atteia
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Rana Alnashwan
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Malak Hassan
- College of Medicine, Alfaisal University, P.O. Box 50927, Riyadh 11533, Saudi Arabia;
| |
Collapse
|
10
|
Abu M, Zahri NAH, Amir A, Ismail MI, Yaakub A, Fukumoto F, Suzuki Y. Analysis of the Effectiveness of Metaheuristic Methods on Bayesian Optimization in the Classification of Visual Field Defects. Diagnostics (Basel) 2023; 13:diagnostics13111946. [PMID: 37296798 DOI: 10.3390/diagnostics13111946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/24/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
Bayesian optimization (BO) is commonly used to optimize the hyperparameters of transfer learning models to improve the model's performance significantly. In BO, the acquisition functions direct the hyperparameter space exploration during the optimization. However, the computational cost of evaluating the acquisition function and updating the surrogate model can become prohibitively expensive due to increasing dimensionality, making it more challenging to achieve the global optimum, particularly in image classification tasks. Therefore, this study investigates and analyses the effect of incorporating metaheuristic methods into BO to improve the performance of acquisition functions in transfer learning. By incorporating four different metaheuristic methods, namely Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) Optimization, Harris Hawks Optimization, and Sailfish Optimization (SFO), the performance of acquisition function, Expected Improvement (EI), was observed in the VGGNet models for visual field defect multi-class classification. Other than EI, comparative observations were also conducted using different acquisition functions, such as Probability Improvement (PI), Upper Confidence Bound (UCB), and Lower Confidence Bound (LCB). The analysis demonstrates that SFO significantly enhanced BO optimization by increasing mean accuracy by 9.6% for VGG-16 and 27.54% for VGG-19. As a result, the best validation accuracy obtained for VGG-16 and VGG-19 is 98.6% and 98.34%, respectively.
Collapse
Affiliation(s)
- Masyitah Abu
- Center of Excellence for Advance Computing, Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, Kangar 01000, Malaysia
| | - Nik Adilah Hanin Zahri
- Center of Excellence for Advance Computing, Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, Kangar 01000, Malaysia
| | - Amiza Amir
- Center of Excellence for Advance Computing, Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, Kangar 01000, Malaysia
| | - Muhammad Izham Ismail
- Institute of Engineering Mathematics, Faculty of Applied and Human Sciences, Universiti Malaysia Perlis, Arau 02600, Malaysia
| | - Azhany Yaakub
- Department of Ophthalmology & Visual Science, Universiti Sains Malaysia, Kubang Kerian 16150, Malaysia
| | - Fumiyo Fukumoto
- Graduate Faculty of Interdisciplinary Research, University of Yamanashi, Kofu 400-0016, Japan
| | - Yoshimi Suzuki
- Graduate Faculty of Interdisciplinary Research, University of Yamanashi, Kofu 400-0016, Japan
| |
Collapse
|
11
|
Ahmed IA, Senan EM, Shatnawi HSA, Alkhraisha ZM, Al-Azzam MMA. Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features. Diagnostics (Basel) 2023; 13:diagnostics13061026. [PMID: 36980334 PMCID: PMC10047564 DOI: 10.3390/diagnostics13061026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/03/2023] [Accepted: 03/06/2023] [Indexed: 03/30/2023] Open
Abstract
Acute lymphoblastic leukemia (ALL) is one of the deadliest forms of leukemia due to the bone marrow producing many white blood cells (WBC). ALL is one of the most common types of cancer in children and adults. Doctors determine the treatment of leukemia according to its stages and its spread in the body. Doctors rely on analyzing blood samples under a microscope. Pathologists face challenges, such as the similarity between infected and normal WBC in the early stages. Manual diagnosis is prone to errors, differences of opinion, and the lack of experienced pathologists compared to the number of patients. Thus, computer-assisted systems play an essential role in assisting pathologists in the early detection of ALL. In this study, systems with high efficiency and high accuracy were developed to analyze the images of C-NMC 2019 and ALL-IDB2 datasets. In all proposed systems, blood micrographs were improved and then fed to the active contour method to extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, and MobileNet). The first strategy for analyzing ALL images of the two datasets is the hybrid technique of CNN-RF and CNN-XGBoost. DenseNet121, ResNet50, and MobileNet models extract deep feature maps. CNN models produce high features with redundant and non-significant features. So, CNN deep feature maps were fed to the Principal Component Analysis (PCA) method to select highly representative features and sent to RF and XGBoost classifiers for classification due to the high similarity between infected and normal WBC in early stages. Thus, the strategy for analyzing ALL images using serially fused features of CNN models. The deep feature maps of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet were merged and then classified by RF classifiers and XGBoost. The RF classifier with fused features for DenseNet121-ResNet50-MobileNet reached an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85% for the C-NMC 2019 dataset. With the ALL-IDB2 dataset, hybrid systems achieved 100% results for AUC, accuracy, sensitivity, precision, and specificity.
Collapse
Affiliation(s)
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen
| | | | | | | |
Collapse
|
12
|
Samee NA, Ahmad T, Mahmoud NF, Atteia G, Abdallah HA, Rizwan A. Clinical Decision Support Framework for Segmentation and Classification of Brain Tumor MRIs Using a U-Net and DCNN Cascaded Learning Algorithm. Healthcare (Basel) 2022; 10:healthcare10122340. [PMID: 36553864 PMCID: PMC9777942 DOI: 10.3390/healthcare10122340] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/11/2022] [Accepted: 11/15/2022] [Indexed: 11/23/2022] Open
Abstract
Brain tumors (BTs) are an uncommon but fatal kind of cancer. Therefore, the development of computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic resonance imaging (MRI) has been the subject of many research papers so far. However, research in this sector is still in its early stage. The ultimate goal of this research is to develop a lightweight effective implementation of the U-Net deep network for use in performing exact real-time segmentation. Moreover, a simplified deep convolutional neural network (DCNN) architecture for the BT classification is presented for automatic feature extraction and classification of the segmented regions of interest (ROIs). Five convolutional layers, rectified linear unit, normalization, and max-pooling layers make up the DCNN's proposed simplified architecture. The introduced method was verified on multimodal brain tumor segmentation (BRATS 2015) datasets. Our experimental results on BRATS 2015 acquired Dice similarity coefficient (DSC) scores, sensitivity, and classification accuracy of 88.8%, 89.4%, and 88.6% for high-grade gliomas. When it comes to segmenting BRATS 2015 BT images, the performance of our proposed CAD framework is on par with existing state-of-the-art methods. However, the accuracy achieved in this study for the classification of BT images has improved upon the accuracy reported in prior studies. Image classification accuracy for BRATS 2015 BT has been improved from 88% to 88.6%.
Collapse
Affiliation(s)
- Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Tahir Ahmad
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan
| | - Noha F. Mahmoud
- Rehabilitation Sciences Department, Health and Rehabilitation Sciences College, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- Correspondence: (N.F.M.); (G.A.); (A.R.)
| | - Ghada Atteia
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
- Correspondence: (N.F.M.); (G.A.); (A.R.)
| | - Hanaa A. Abdallah
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Atif Rizwan
- Department of Computer Engineering, Jeju National University, Jejusi 63243, Republic of Korea
- Correspondence: (N.F.M.); (G.A.); (A.R.)
| |
Collapse
|
13
|
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.
Collapse
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.)
| |
Collapse
|