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Li Y, Liu H, Lv Q, Long J. Diagnosis model of early Pneumocystis jirovecii pneumonia based on convolutional neural network: a comparison with traditional PCR diagnostic method. BMC Pulm Med 2024; 24:205. [PMID: 38664747 PMCID: PMC11046959 DOI: 10.1186/s12890-024-02987-x] [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: 12/01/2023] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Pneumocystis jirovecii pneumonia (PJP) is an interstitial pneumonia caused by pneumocystis jirovecii (PJ). The diagnosis of PJP primarily relies on the detection of the pathogen from lower respiratory tract specimens. However, it faces challenges such as difficulty in obtaining specimens and low detection rates. In the clinical diagnosis process, it is necessary to combine clinical symptoms, serological test results, chest Computed tomography (CT) images, molecular biology techniques, and metagenomics next-generation sequencing (mNGS) for comprehensive analysis. PURPOSE This study aims to overcome the limitations of traditional PJP diagnosis methods and develop a non-invasive, efficient, and accurate diagnostic approach for PJP. By using this method, patients can receive early diagnosis and treatment, effectively improving their prognosis. METHODS We constructed an intelligent diagnostic model for PJP based on the different Convolutional Neural Networks. Firstly, we used the Convolutional Neural Network to extract CT image features from patients. Then, we fused the CT image features with clinical information features using a feature fusion function. Finally, the fused features were input into the classification network to obtain the patient's diagnosis result. RESULTS In this study, for the diagnosis of PJP, the accuracy of the traditional PCR diagnostic method is 77.58%, while the mean accuracy of the optimal diagnostic model based on convolutional neural networks is 88.90%. CONCLUSION The accuracy of the diagnostic method proposed in this paper is 11.32% higher than that of the traditional PCR diagnostic method. The method proposed in this paper is an efficient, accurate, and non-invasive early diagnosis approach for PJP.
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Affiliation(s)
- Yingying Li
- Department of Clinical Laboratory, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Hailin Liu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Department of Information, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Qingwen Lv
- Department of Information, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
| | - Jun Long
- Department of Clinical Laboratory, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
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Alsubai S. Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques. PeerJ Comput Sci 2024; 10:e1996. [PMID: 38660170 PMCID: PMC11042027 DOI: 10.7717/peerj-cs.1996] [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: 11/28/2023] [Accepted: 03/27/2024] [Indexed: 04/26/2024]
Abstract
Cancer, a life-threatening disorder caused by genetic abnormalities and metabolic irregularities, is a substantial health danger, with lung and colon cancer being major contributors to death. Histopathological identification is critical in directing effective treatment regimens for these cancers. The earlier these disorders are identified, the lesser the risk of death. The use of machine learning and deep learning approaches has the potential to speed up cancer diagnosis processes by allowing researchers to analyse large patient databases quickly and affordably. This study introduces the Inception-ResNetV2 model with strategically incorporated local binary patterns (LBP) features to improve diagnostic accuracy for lung and colon cancer identification. The model is trained on histopathological images, and the integration of deep learning and texture-based features has demonstrated its exceptional performance with 99.98% accuracy. Importantly, the study employs explainable artificial intelligence (AI) through SHapley Additive exPlanations (SHAP) to unravel the complex inner workings of deep learning models, providing transparency in decision-making processes. This study highlights the potential to revolutionize cancer diagnosis in an era of more accurate and reliable medical assessments.
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Affiliation(s)
- Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
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Alyami J. Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions. EJNMMI REPORTS 2024; 8:7. [PMID: 38748374 PMCID: PMC10982256 DOI: 10.1186/s41824-024-00195-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 02/05/2024] [Indexed: 05/19/2024]
Abstract
Radiological image analysis using machine learning has been extensively applied to enhance biopsy diagnosis accuracy and assist radiologists with precise cures. With improvements in the medical industry and its technology, computer-aided diagnosis (CAD) systems have been essential in detecting early cancer signs in patients that could not be observed physically, exclusive of introducing errors. CAD is a detection system that combines artificially intelligent techniques with image processing applications thru computer vision. Several manual procedures are reported in state of the art for cancer diagnosis. Still, they are costly, time-consuming and diagnose cancer in late stages such as CT scans, radiography, and MRI scan. In this research, numerous state-of-the-art approaches on multi-organs detection using clinical practices are evaluated, such as cancer, neurological, psychiatric, cardiovascular and abdominal imaging. Additionally, numerous sound approaches are clustered together and their results are assessed and compared on benchmark datasets. Standard metrics such as accuracy, sensitivity, specificity and false-positive rate are employed to check the validity of the current models reported in the literature. Finally, existing issues are highlighted and possible directions for future work are also suggested.
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Affiliation(s)
- Jaber Alyami
- Department of Radiological Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
- King Fahd Medical Research Center, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
- Smart Medical Imaging Research Group, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
- Medical Imaging and Artificial Intelligence Research Unit, Center of Modern Mathematical Sciences and its Applications, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
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Al-Otaibi S, Rehman A, Mujahid M, Alotaibi S, Saba T. Efficient-gastro: optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy images. PeerJ Comput Sci 2024; 10:e1902. [PMID: 38660212 PMCID: PMC11041956 DOI: 10.7717/peerj-cs.1902] [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: 11/21/2023] [Accepted: 01/31/2024] [Indexed: 04/26/2024]
Abstract
Gastrointestinal diseases cause around two million deaths globally. Wireless capsule endoscopy is a recent advancement in medical imaging, but manual diagnosis is challenging due to the large number of images generated. This has led to research into computer-assisted methodologies for diagnosing these images. Endoscopy produces thousands of frames for each patient, making manual examination difficult, laborious, and error-prone. An automated approach is essential to speed up the diagnosis process, reduce costs, and potentially save lives. This study proposes transfer learning-based efficient deep learning methods for detecting gastrointestinal disorders from multiple modalities, aiming to detect gastrointestinal diseases with superior accuracy and reduce the efforts and costs of medical experts. The Kvasir eight-class dataset was used for the experiment, where endoscopic images were preprocessed and enriched with augmentation techniques. An EfficientNet model was optimized via transfer learning and fine tuning, and the model was compared to the most widely used pre-trained deep learning models. The model's efficacy was tested on another independent endoscopic dataset to prove its robustness and reliability.
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Affiliation(s)
- Shaha Al-Otaibi
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Amjad Rehman
- Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia
| | - Muhammad Mujahid
- Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia
| | - Sarah Alotaibi
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Tanzila Saba
- Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia
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5
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Saidani O, Umer M, Alturki N, Alshardan A, Kiran M, Alsubai S, Kim TH, Ashraf I. White blood cells classification using multi-fold pre-processing and optimized CNN model. Sci Rep 2024; 14:3570. [PMID: 38347011 PMCID: PMC10861568 DOI: 10.1038/s41598-024-52880-0] [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: 08/26/2023] [Accepted: 01/24/2024] [Indexed: 02/15/2024] Open
Abstract
White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works.
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Affiliation(s)
- Oumaima Saidani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Muhammad Umer
- Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
| | - Nazik Alturki
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Amal Alshardan
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Muniba Kiran
- Department of Biotechnology, Virtual University of Pakistan, M.A. Jinnah Campus, Defence Road, Off Raiwind Road, Lahore, 54000, Pakistan
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, P.O. Box 151, 11942, Al-Kharj, Saudi Arabia
| | - Tai-Hoon Kim
- School of Electrical and Computer Engineering, Yeosu Campus, Chonnam National University, 50, Daehak-ro, Yeosu-si, Jeollanam-do, 59626, Republic of Korea.
| | - Imran Ashraf
- Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea.
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An Q, Chen W, Shao W. A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble. Diagnostics (Basel) 2024; 14:390. [PMID: 38396430 PMCID: PMC10887593 DOI: 10.3390/diagnostics14040390] [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: 01/04/2024] [Revised: 02/07/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in the efficacy of medical decision systems. This paper presents a novel approach utilizing a deep convolutional neural network that effectively amalgamates the strengths of EfficientNetB0 and DenseNet121, and it is enhanced by a suite of attention mechanisms for refined pneumonia image classification. Leveraging pre-trained models, our network employs multi-head, self-attention modules for meticulous feature extraction from X-ray images. The model's integration and processing efficiency are further augmented by a channel-attention-based feature fusion strategy, one that is complemented by a residual block and an attention-augmented feature enhancement and dynamic pooling strategy. Our used dataset, which comprises a comprehensive collection of chest X-ray images, represents both healthy individuals and those affected by pneumonia, and it serves as the foundation for this research. This study delves deep into the algorithms, architectural details, and operational intricacies of the proposed model. The empirical outcomes of our model are noteworthy, with an exceptional performance marked by an accuracy of 95.19%, a precision of 98.38%, a recall of 93.84%, an F1 score of 96.06%, a specificity of 97.43%, and an AUC of 0.9564 on the test dataset. These results not only affirm the model's high diagnostic accuracy, but also highlight its promising potential for real-world clinical deployment.
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Affiliation(s)
- Qiuyu An
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;
| | - Wei Chen
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;
| | - Wei Shao
- Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute, Shenzhen 518067, China
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Karamti H, Alharthi R, Umer M, Shaiba H, Ishaq A, Abuzinadah N, Alsubai S, Ashraf I. Breast cancer detection employing stacked ensemble model with convolutional features. Cancer Biomark 2024; 40:155-170. [PMID: 38160347 DOI: 10.3233/cbm-230294] [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: 01/03/2024]
Abstract
Breast cancer is a major cause of female deaths, especially in underdeveloped countries. It can be treated if diagnosed early and chances of survival are high if treated appropriately and timely. For timely and accurate automated diagnosis, machine learning approaches tend to show better results than traditional methods, however, accuracy lacks the desired level. This study proposes the use of an ensemble model to provide accurate detection of breast cancer. The proposed model uses the random forest and support vector classifier along with automatic feature extraction using an optimized convolutional neural network (CNN). Extensive experiments are performed using the original, as well as, CNN-based features to analyze the performance of the deployed models. Experimental results involving the use of the Wisconsin dataset reveal that CNN-based features provide better results than the original features. It is observed that the proposed model achieves an accuracy of 99.99% for breast cancer detection. Performance comparison with existing state-of-the-art models is also carried out showing the superior performance of the proposed model.
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Affiliation(s)
- Hanen Karamti
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Raed Alharthi
- Department of Computer Science and Engineering, University of Hafr Al-Batin, Hafar, Saudi Arabia
| | - Muhammad Umer
- Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Hadil Shaiba
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Abid Ishaq
- Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Nihal Abuzinadah
- Faculty of Computer Science and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Korea
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Zhan Y, Wang H, Wu Z, Zeng Z. Study on the Common Molecular Mechanism of Metabolic Acidosis and Myocardial Damage Complicated by Neonatal Pneumonia. Metabolites 2023; 13:1118. [PMID: 37999214 PMCID: PMC10673214 DOI: 10.3390/metabo13111118] [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: 09/25/2023] [Revised: 10/09/2023] [Accepted: 10/11/2023] [Indexed: 11/25/2023] Open
Abstract
Pneumonia is a common clinical disease in the neonatal period and poses a serious risk to infant health. Therefore, the understanding of molecular mechanisms is of great importance for the development of methods for the rapid and accurate identification, classification and staging, and even disease diagnosis and therapy of pneumonia. In this study, a nontargeted metabonomic method was developed and applied for the analysis of serum samples collected from 20 cases in the pneumonia control group (PN) and 20 and 10 cases of pneumonia patients with metabolic acidosis (MA) and myocardial damage (MD), respectively, with the help of ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS). The results showed that compared with the pneumonia group, 23 and 21 differential metabolites were identified in pneumonia with two complications. They showed high sensitivity and specificity, with the area under the curve (ROC) of the receiver operating characteristic curve (ROC) larger than 0.7 for each differential molecule. There were 14 metabolites and three metabolic pathways of sphingolipid metabolism, porphyrin and chlorophyll metabolism, and glycerophospholipid metabolism existing in both groups of PN and MA, and PN and MD, all involving significant changes in pathways closely related to amino acid metabolism disorders, abnormal cell apoptosis, and inflammatory responses. These findings of molecular mechanisms should help a lot to fully understand and even treat the complications of pneumonia in infants.
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Affiliation(s)
- Yifei Zhan
- College of Environmental and Chemical Engineering, Dalian University, Dalian 116622, China;
| | - Huaiyan Wang
- Department of Neonatology, Changzhou Medical Center, Changzhou Maternity and Child Health Care Hospital, Nanjing Medical University, Changzhou 213000, China;
| | - Zeying Wu
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry & Chemical Engineering, Nanjing University, Nanjing 210023, China
- School of Chemical Engineering and Material Sciences, Changzhou Institute of Technology, Changzhou 213032, China
| | - Zhongda Zeng
- College of Environmental and Chemical Engineering, Dalian University, Dalian 116622, China;
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Majeed F, Shafique U, Safran M, Alfarhood S, Ashraf I. Detection of Drowsiness among Drivers Using Novel Deep Convolutional Neural Network Model. SENSORS (BASEL, SWITZERLAND) 2023; 23:8741. [PMID: 37960441 PMCID: PMC10650052 DOI: 10.3390/s23218741] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 10/20/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023]
Abstract
Detecting drowsiness among drivers is critical for ensuring road safety and preventing accidents caused by drowsy or fatigued driving. Research on yawn detection among drivers has great significance in improving traffic safety. Although various studies have taken place where deep learning-based approaches are being proposed, there is still room for improvement to develop better and more accurate drowsiness detection systems using behavioral features such as mouth and eye movement. This study proposes a deep neural network architecture for drowsiness detection employing a convolutional neural network (CNN) for driver drowsiness detection. Experiments involve using the DLIB library to locate key facial points to calculate the mouth aspect ratio (MAR). To compensate for the small dataset, data augmentation is performed for the 'yawning' and 'no_yawning' classes. Models are trained and tested involving the original and augmented dataset to analyze the impact on model performance. Experimental results demonstrate that the proposed CNN model achieves an average accuracy of 96.69%. Performance comparison with existing state-of-the-art approaches shows better performance of the proposed model.
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Affiliation(s)
- Fiaz Majeed
- Department of Information Technology, University of Gujrat, Gujrat 50700, Pakistan; (F.M.); (U.S.)
| | - Umair Shafique
- Department of Information Technology, University of Gujrat, Gujrat 50700, Pakistan; (F.M.); (U.S.)
| | - Mejdl Safran
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia;
| | - Sultan Alfarhood
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia;
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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Uppal M, Gupta D, Juneja S, Gadekallu TR, El Bayoumy I, Hussain J, Lee SW. Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers. Front Bioeng Biotechnol 2023; 11:1257591. [PMID: 37823024 PMCID: PMC10564587 DOI: 10.3389/fbioe.2023.1257591] [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: 07/12/2023] [Accepted: 09/04/2023] [Indexed: 10/13/2023] Open
Abstract
The human brain is an extremely intricate and fascinating organ that is made up of the cerebrum, cerebellum, and brainstem and is protected by the skull. Brain stroke is recognized as a potentially fatal condition brought on by an unfavorable obstruction in the arteries supplying the brain. The severity of brain stroke may be reduced or controlled with its early prognosis to lessen the mortality rate and lead to good health. This paper proposed a technique to predict brain strokes with high accuracy. The model was constructed using data related to brain strokes. The aim of this work is to use Multi Layer Perceptron (MLP) as a classification technique for stroke data and used multi-optimizers that include Adaptive moment estimation with Maximum (AdaMax), Root Mean Squared Propagation (RMSProp) and Adaptive learning rate method (Adadelta). The experiment shows RMSProp optimizer is best with a data training accuracy of 95.8% and a value for data testing accuracy of 94.9%. The novelty of work is to incorporate multiple optimizers alongside the MLP classifier which offers a comprehensive approach to stroke prediction, providing a more robust and accurate solution. The obtained results underscore the effectiveness of the proposed methodology in enhancing the accuracy of brain stroke detection, thereby paving the way for potential advancements in medical diagnosis and treatment.
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Affiliation(s)
- Mudita Uppal
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Deepali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | | | - Thippa Reddy Gadekallu
- Zhongda Group, Jiaxing, Zhejiang, China
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
- College of Information Science and Engineering, Jiaxing University, Jiaxing, China
- Division of Research and Development, Lovely Professional University, Phagwara, India
| | - Ibrahim El Bayoumy
- Public Health and Community Medicine, Tanta Faculty of Medicine, Tanta, Egypt
| | - Jamil Hussain
- Department of Data Science, Sejong University, Seoul, Republic of Korea
| | - Seung Won Lee
- Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
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