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Rajadurai S, Perumal K, Ijaz MF, Chowdhary CL. PrecisionLymphoNet: Advancing Malignant Lymphoma Diagnosis via Ensemble Transfer Learning with CNNs. Diagnostics (Basel) 2024; 14:469. [PMID: 38472941 DOI: 10.3390/diagnostics14050469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/26/2024] [Accepted: 01/27/2024] [Indexed: 03/14/2024] Open
Abstract
Malignant lymphoma, which impacts the lymphatic system, presents diverse challenges in accurate diagnosis due to its varied subtypes-chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). Lymphoma is a form of cancer that begins in the lymphatic system, impacting lymphocytes, which are a specific type of white blood cell. This research addresses these challenges by proposing ensemble and non-ensemble transfer learning models employing pre-trained weights from VGG16, VGG19, DenseNet201, InceptionV3, and Xception. For the ensemble technique, this paper adopts a stack-based ensemble approach. It is a two-level classification approach and best suited for accuracy improvement. Testing on a multiclass dataset of CLL, FL, and MCL reveals exceptional diagnostic accuracy, with DenseNet201, InceptionV3, and Xception exceeding 90% accuracy. The proposed ensemble model, leveraging InceptionV3 and Xception, achieves an outstanding 99% accuracy over 300 epochs, surpassing previous prediction methods. This study demonstrates the feasibility and efficiency of the proposed approach, showcasing its potential in real-world medical applications for precise lymphoma diagnosis.
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Affiliation(s)
- Sivashankari Rajadurai
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India
| | - Kumaresan Perumal
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India
| | - Muhammad Fazal Ijaz
- School of IT and Engineering, Melbourne Institute of Technology, Melbourne, VIC 3000, Australia
| | - Chiranji Lal Chowdhary
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India
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Xu F, Zheng Q, Shi J, Yan K, Wang M. Pre-training and ensembling based Alzheimer's disease detection. Technol Health Care 2024; 32:379-395. [PMID: 37545287 DOI: 10.3233/thc-230571] [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] [Indexed: 08/08/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) endangers the physical and mental health of the elderly, constituting one of the most crucial social challenges. Due to lack of effective AD intervention drugs, it is very important to diagnose AD in the early stage, especially in the Mild Cognitive Impairment (MCI) phase. OBJECTIVE At present, an automatic classification technology is urgently needed to assist doctors in analyzing the status of the candidate patient. The artificial intelligence enhanced Alzheimer's disease detection can reduce costs to detect Alzheimer's disease. METHODS In this paper, a novel pre-trained ensemble-based AD detection (PEADD) framework with three base learners (i.e., ResNet, VGG, and EfficientNet) for both the audio-based and PET (Positron Emission Tomography)-based AD detection is proposed under a unified image modality. Specifically, the effectiveness of context-enriched image modalities instead of the traditional speech modality (i.e., context-free audio matrix) for the audio-based AD detection, along with simple and efficient image denoising strategy has been inspected comprehensively. Meanwhile, the PET-based AD detection based on the denoised PET image has been described. Furthermore, different voting methods for applying an ensemble strategy (i.e., hard voting and soft voting) has been investigated in detail. RESULTS The results showed that the classification accuracy was 92% and 99% on the audio-based and PET-based AD datasets, respectively. Our extensive experimental results demonstrate that our PEADD outperforms the state-of-the-art methods on both audio-based and PET-based AD datasets simultaneously. CONCLUSIONS The network model can provide an objective basis for doctors to detect Alzheimer's Disease.
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Yuan J, Zhang Y, Wang X. Application of machine learning in the management of lymphoma: Current practice and future prospects. Digit Health 2024; 10:20552076241247963. [PMID: 38628632 PMCID: PMC11020711 DOI: 10.1177/20552076241247963] [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: 12/04/2023] [Accepted: 03/28/2024] [Indexed: 04/19/2024] Open
Abstract
In the past decade, digitization of medical records and multiomics data analysis in lymphoma has led to the accessibility of high-dimensional records. The digitization of medical records, the visualization of extensive volume data extracted from medical images, and the integration of multiomics methods into clinical decision-making have produced many datasets. As a promising auxiliary tool, machine learning (ML) intends to extract homologous features in large-scale data sets and encode them into various patterns to complete complicated tasks. At present, artificial intelligence and digital mining have shown promising prospects in the field of lymphoma pathological image analysis. The paradigm shift from qualitative analysis to quantitative analysis makes the pathological diagnosis more intelligent and the results more accurate and objective. ML can promote accurate lymphoma diagnosis and provide patients with prognostic information and more individualized treatment options. Based on the above, this comprehensive review of the general workflow of ML highlights recent advances in ML techniques in the diagnosis, treatment, and prognosis of lymphoma, and clarifies the boundedness and future orientation of the ML technique in the clinical practice of lymphoma.
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Affiliation(s)
- Junyun Yuan
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Ya Zhang
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Taishan Scholars Program of Shandong Province, Jinan, Shandong, China
| | - Xin Wang
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Taishan Scholars Program of Shandong Province, Jinan, Shandong, China
- Branch of National Clinical Research Center for Hematologic Diseases, Jinan, Shandong, China
- National Clinical Research Center for Hematologic Diseases, Hospital of Soochow University, Suzhou, China
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Hamdi M, Senan EM, Jadhav ME, Olayah F, Awaji B, Alalayah KM. Hybrid Models Based on Fusion Features of a CNN and Handcrafted Features for Accurate Histopathological Image Analysis for Diagnosing Malignant Lymphomas. Diagnostics (Basel) 2023; 13:2258. [PMID: 37443652 DOI: 10.3390/diagnostics13132258] [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: 05/24/2023] [Revised: 06/10/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
Malignant lymphoma is one of the most severe types of disease that leads to death as a result of exposure of lymphocytes to malignant tumors. The transformation of cells from indolent B-cell lymphoma to B-cell lymphoma (DBCL) is life-threatening. Biopsies taken from the patient are the gold standard for lymphoma analysis. Glass slides under a microscope are converted into whole slide images (WSI) to be analyzed by AI techniques through biomedical image processing. Because of the multiplicity of types of malignant lymphomas, manual diagnosis by pathologists is difficult, tedious, and subject to disagreement among physicians. The importance of artificial intelligence (AI) in the early diagnosis of malignant lymphoma is significant and has revolutionized the field of oncology. The use of AI in the early diagnosis of malignant lymphoma offers numerous benefits, including improved accuracy, faster diagnosis, and risk stratification. This study developed several strategies based on hybrid systems to analyze histopathological images of malignant lymphomas. For all proposed models, the images and extraction of malignant lymphocytes were optimized by the gradient vector flow (GVF) algorithm. The first strategy for diagnosing malignant lymphoma images relied on a hybrid system between three types of deep learning (DL) networks, XGBoost algorithms, and decision tree (DT) algorithms based on the GVF algorithm. The second strategy for diagnosing malignant lymphoma images was based on fusing the features of the MobileNet-VGG16, VGG16-AlexNet, and MobileNet-AlexNet models and classifying them by XGBoost and DT algorithms based on the ant colony optimization (ACO) algorithm. The color, shape, and texture features, which are called handcrafted features, were extracted by four traditional feature extraction algorithms. Because of the similarity in the biological characteristics of early-stage malignant lymphomas, the features of the fused MobileNet-VGG16, VGG16-AlexNet, and MobileNet-AlexNet models were combined with the handcrafted features and classified by the XGBoost and DT algorithms based on the ACO algorithm. We concluded that the performance of the two networks XGBoost and DT, with fused features between DL networks and handcrafted, achieved the best performance. The XGBoost network based on the fused features of MobileNet-VGG16 and handcrafted features resulted in an AUC of 99.43%, accuracy of 99.8%, precision of 99.77%, sensitivity of 99.7%, and specificity of 99.8%. This highlights the significant role of AI in the early diagnosis of malignant lymphoma, offering improved accuracy, expedited diagnosis, and enhanced risk stratification. This study highlights leveraging AI techniques and biomedical image processing; the analysis of whole slide images (WSI) converted from biopsies allows for improved accuracy, faster diagnosis, and risk stratification. The developed strategies based on hybrid systems, combining deep learning networks, XGBoost and decision tree algorithms, demonstrated promising results in diagnosing malignant lymphoma images. Furthermore, the fusion of handcrafted features with features extracted from DL networks enhanced the performance of the classification models.
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Affiliation(s)
- Mohammed Hamdi
- Department of Computer Science, Faculty of 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
| | - Mukti E Jadhav
- Shri Shivaji Science & Arts College, Chikhli Dist., Buldana 443201, India
| | - Fekry Olayah
- Department of Information System, Faculty Computer Science and Information System, Najran University, Najran 66462, Saudi Arabia
| | - Bakri Awaji
- Department of Computer Science, Faculty of Computer Science and Information System, Najran University, Najran 66462, Saudi Arabia
| | - Khaled M Alalayah
- Department of Computer Science, Faculty of Science and Arts, Sharurah, Najran University, Najran 66462, Saudi Arabia
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Carreras J, Roncador G, Hamoudi R. Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels. Cancers (Basel) 2022; 14:5318. [PMID: 36358737 PMCID: PMC9657332 DOI: 10.3390/cancers14215318] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 08/01/2023] Open
Abstract
Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made.
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Affiliation(s)
- Joaquim Carreras
- Department of Pathology, School of Medicine, Tokai University, 143 Shimokasuya, Isehara 259-1193, Kanagawa, Japan
| | - Giovanna Roncador
- Monoclonal Antibodies Unit, Spanish National Cancer Research Center (Centro Nacional de Investigaciones Oncologicas, CNIO), Melchor Fernandez Almagro 3, 28029 Madrid, Spain
| | - Rifat Hamoudi
- Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
- Division of Surgery and Interventional Science, University College London, Gower Street, London WC1E 6BT, UK
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Khelil H, El Moumene Zerari A, Djerou L. Accurate diagnosis of non-Hodgkin lymphoma on whole-slide images using deep learning. 2022 IEEE 9TH INTERNATIONAL CONFERENCE ON SCIENCES OF ELECTRONICS, TECHNOLOGIES OF INFORMATION AND TELECOMMUNICATIONS (SETIT) 2022. [DOI: 10.1109/setit54465.2022.9875482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
- Hathem Khelil
- Mohamed Boudiaf University of M’sila,Laboratory of Informatics and its Applications of M’sila (LIAM),Algeria
| | | | - Leila Djerou
- University of Biskra,LISIA Laboratory,Computer Science Department,Algeria
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