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Rai HM, Yoo J, Razaque A. Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques. EXPERT SYSTEMS WITH APPLICATIONS 2024; 255:124838. [DOI: 10.1016/j.eswa.2024.124838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024] [Imported: 09/11/2024]
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Moqurrab SA, Rai HM, Yoo J. HRIDM: Hybrid Residual/Inception-Based Deeper Model for Arrhythmia Detection from Large Sets of 12-Lead ECG Recordings. ALGORITHMS 2024; 17:364. [DOI: 10.3390/a17080364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024] [Imported: 09/11/2024]
Abstract
Heart diseases such as cardiovascular and myocardial infarction are the foremost reasons of death in the world. The timely, accurate, and effective prediction of heart diseases is crucial for saving lives. Electrocardiography (ECG) is a primary non-invasive method to identify cardiac abnormalities. However, manual interpretation of ECG recordings for heart disease diagnosis is a time-consuming and inaccurate process. For the accurate and efficient detection of heart diseases from the 12-lead ECG dataset, we have proposed a hybrid residual/inception-based deeper model (HRIDM). In this study, we have utilized ECG datasets from various sources, which are multi-institutional large ECG datasets. The proposed model is trained on 12-lead ECG data from over 10,000 patients. We have compared the proposed model with several state-of-the-art (SOTA) models, such as LeNet-5, AlexNet, VGG-16, ResNet-50, Inception, and LSTM, on the same training and test datasets. To show the effectiveness of the computational efficiency of the proposed model, we have only trained over 20 epochs without GPU support and we achieved an accuracy of 50.87% on the test dataset for 27 categories of heart abnormalities. We found that our proposed model outperformed the previous studies which participated in the official PhysioNet/CinC Challenge 2020 and achieved fourth place as compared with the 41 official ranking teams. The result of this study indicates that the proposed model is an implying new method for predicting heart diseases using 12-lead ECGs.
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Rai HM, Yoo J, Razaque A. A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction. Med Biol Eng Comput 2024:10.1007/s11517-024-03158-0. [PMID: 39012415 DOI: 10.1007/s11517-024-03158-0] [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: 02/21/2024] [Accepted: 06/22/2024] [Indexed: 07/17/2024] [Imported: 09/11/2024]
Abstract
The fight against cancer, a relentless global health crisis, emphasizes the urgency for efficient and automated early detection methods. To address this critical need, this review assesses recent advances in non-invasive cancer prediction techniques, comparing conventional machine learning (CML) and deep neural networks (DNNs). Focusing on these seven major cancers, we analyze 310 publications spanning the years 2018 to 2024, focusing on detection accuracy as the key metric to identify the most effective predictive models, highlighting critical gaps in current methodologies, and suggesting directions for future research. We further delved into factors like datasets, features, and modalities to gain a comprehensive understanding of each approach's performance. Separate review tables for each cancer type and approach facilitated comparisons between top performers (accuracy exceeding 99%) and low performers (65.83 to 85.8%). Our exploration of public databases and commonly used classifiers revealed that optimal combinations of features, datasets, and models can achieve up to 100% accuracy for both CML and DNN. However, significant variations in accuracy (up to 35%) were observed, particularly when optimization was lacking. Notably, colorectal cancer exhibited the lowest accuracy (DNN 69%, CML 65.83%). A five-point comparative analysis (best/worst models, performance gap, average accuracy, and research trends) revealed that while DNN research is gaining momentum, CML approaches remain competitive, even outperforming DNN in some cases. This study presents an in-depth comparative analysis of CML and DNN techniques for cancer detection. This knowledge can inform future research directions and contribute to the development of increasingly accurate and reliable cancer detection tools.
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Rai HM, Yoo J, Dashkevych S. Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling. J Cancer Res Clin Oncol 2024; 150:220. [PMID: 38684578 PMCID: PMC11058623 DOI: 10.1007/s00432-024-05718-1] [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: 07/31/2023] [Accepted: 03/21/2024] [Indexed: 05/02/2024] [Imported: 09/11/2024]
Abstract
PURPOSE The purpose of this study is to develop accurate and automated detection and segmentation methods for brain tumors, given their significant fatality rates, with aggressive malignant tumors like Glioblastoma Multiforme (GBM) having a five-year survival rate as low as 5 to 10%. This underscores the urgent need to improve diagnosis and treatment outcomes through innovative approaches in medical imaging and deep learning techniques. METHODS In this work, we propose a novel approach utilizing the two-headed UNetEfficientNets model for simultaneous segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) images. The model combines the strengths of EfficientNets and a modified two-headed Unet model. We utilized a publicly available dataset consisting of 3064 brain MR images classified into three tumor classes: Meningioma, Glioma, and Pituitary. To enhance the training process, we performed 12 types of data augmentation on the training dataset. We evaluated the methodology using six deep learning models, ranging from UNetEfficientNet-B0 to UNetEfficientNet-B5, optimizing the segmentation and classification heads using binary cross entropy (BCE) loss with Dice and BCE with focal loss, respectively. Post-processing techniques such as connected component labeling (CCL) and ensemble models were applied to improve segmentation outcomes. RESULTS The proposed UNetEfficientNet-B4 model achieved outstanding results, with an accuracy of 99.4% after postprocessing. Additionally, it obtained high scores for DICE (94.03%), precision (98.67%), and recall (99.00%) after post-processing. The ensemble technique further improved segmentation performance, with a global DICE score of 95.70% and Jaccard index of 91.20%. CONCLUSION Our study demonstrates the high efficiency and accuracy of the proposed UNetEfficientNet-B4 model in the automatic and parallel detection and segmentation of brain tumors from MRI images. This approach holds promise for improving diagnosis and treatment planning for patients with brain tumors, potentially leading to better outcomes and prognosis.
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Rai HM, Yoo J, Atif Moqurrab S, Dashkevych S. Advancements in traditional machine learning techniques for detection and diagnosis of fatal cancer types: Comprehensive review of biomedical imaging datasets. MEASUREMENT 2024; 225:114059. [DOI: 10.1016/j.measurement.2023.114059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024] [Imported: 09/11/2024]
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Rai HM, Yoo J. Analysis of Colorectal and Gastric Cancer Classification: A Mathematical Insight Utilizing Traditional Machine Learning Classifiers. MATHEMATICS 2023; 11:4937. [DOI: 10.3390/math11244937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024] [Imported: 09/11/2024]
Abstract
Cancer remains a formidable global health challenge, claiming millions of lives annually. Timely and accurate cancer diagnosis is imperative. While numerous reviews have explored cancer classification using machine learning and deep learning techniques, scant literature focuses on traditional ML methods. In this manuscript, we undertake a comprehensive review of colorectal and gastric cancer detection specifically employing traditional ML classifiers. This review emphasizes the mathematical underpinnings of cancer detection, encompassing preprocessing techniques, feature extraction, machine learning classifiers, and performance assessment metrics. We provide mathematical formulations for these key components. Our analysis is limited to peer-reviewed articles published between 2017 and 2023, exclusively considering medical imaging datasets. Benchmark and publicly available imaging datasets for colorectal and gastric cancers are presented. This review synthesizes findings from 20 articles on colorectal cancer and 16 on gastric cancer, culminating in a total of 36 research articles. A significant focus is placed on mathematical formulations for commonly used preprocessing techniques, features, ML classifiers, and assessment metrics. Crucially, we introduce our optimized methodology for the detection of both colorectal and gastric cancers. Our performance metrics analysis reveals remarkable results: 100% accuracy in both cancer types, but with the lowest sensitivity recorded at 43.1% for gastric cancer.
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Rai HM, Yoo J. A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics. J Cancer Res Clin Oncol 2023; 149:14365-14408. [PMID: 37540254 DOI: 10.1007/s00432-023-05216-w] [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: 06/20/2023] [Accepted: 07/26/2023] [Indexed: 08/05/2023]
Abstract
PURPOSE There are millions of people who lose their life due to several types of fatal diseases. Cancer is one of the most fatal diseases which may be due to obesity, alcohol consumption, infections, ultraviolet radiation, smoking, and unhealthy lifestyles. Cancer is abnormal and uncontrolled tissue growth inside the body which may be spread to other body parts other than where it has originated. Hence it is very much required to diagnose the cancer at an early stage to provide correct and timely treatment. Also, manual diagnosis and diagnostic error may cause of the death of many patients hence much research are going on for the automatic and accurate detection of cancer at early stage. METHODS In this paper, we have done the comparative analysis of the diagnosis and recent advancement for the detection of various cancer types using traditional machine learning (ML) and deep learning (DL) models. In this study, we have included four types of cancers, brain, lung, skin, and breast and their detection using ML and DL techniques. In extensive review we have included a total of 130 pieces of literature among which 56 are of ML-based and 74 are from DL-based cancer detection techniques. Only the peer reviewed research papers published in the recent 5-year span (2018-2023) have been included for the analysis based on the parameters, year of publication, feature utilized, best model, dataset/images utilized, and best accuracy. We have reviewed ML and DL-based techniques for cancer detection separately and included accuracy as the performance evaluation metrics to maintain the homogeneity while verifying the classifier efficiency. RESULTS Among all the reviewed literatures, DL techniques achieved the highest accuracy of 100%, while ML techniques achieved 99.89%. The lowest accuracy achieved using DL and ML approaches were 70% and 75.48%, respectively. The difference in accuracy between the highest and lowest performing models is about 28.8% for skin cancer detection. In addition, the key findings, and challenges for each type of cancer detection using ML and DL techniques have been presented. The comparative analysis between the best performing and worst performing models, along with overall key findings and challenges, has been provided for future research purposes. Although the analysis is based on accuracy as the performance metric and various parameters, the results demonstrate a significant scope for improvement in classification efficiency. CONCLUSION The paper concludes that both ML and DL techniques hold promise in the early detection of various cancer types. However, the study identifies specific challenges that need to be addressed for the widespread implementation of these techniques in clinical settings. The presented results offer valuable guidance for future research in cancer detection, emphasizing the need for continued advancements in ML and DL-based approaches to improve diagnostic accuracy and ultimately save more lives.
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Rai HM, Atik-Ur-Rehman, Pal A, Mishra S, Shukla KK. Use of Internet of Things in the context of execution of smart city applications: a review. DISCOVER INTERNET OF THINGS 2023; 3:8. [DOI: 10.1007/s43926-023-00037-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 08/21/2023] [Indexed: 09/11/2024] [Imported: 09/11/2024]
Abstract
AbstractThe Internet of Things (IoT) is rapidly becoming one of the most talked-about and essential components of any digitization process. The IoT is comprised of several key necessary components, the most important of which are sensors, communication (the internet), and user interfaces for data processing. IoTs are currently finding applications in virtually every industry, including healthcare, where they are known as the internet of medical things (IoMT), industry, where they are known as the industrial internet of things (IIoT), and interconnection between people, where they are known as the internet of everything (IoE). The challenge is to leverage the Internet of Things (IoT), technology, and data to create smarter and more sustainable cities that enhance the quality of life for residents. Therefore, in this article; we have demonstrated the use of the IoT in a variety of applications for smart communities. These applications include smart transportation, smart water management, smart garbage management, smart house illumination, smart parking, smart infrastructure, etc. This research also includes an explanation of the flow process of implementing the IoT in different applications of smart communities, as well as their characteristics and particular applications. Along with their flow illustration, the stages involved in the implementation of smart city applications and the components they consist of are also displayed here. We have also taken into consideration the instances of particular cases and their implementation utilizing IoT. Some of these cases include the automated water collection methods of smart water management systems as well as the condition of the water. Based on the findings of the research, we came to the conclusion that IoT devices play an essential role in each and every one of the smart city project implementations.
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Rai HM. Cancer detection and segmentation using machine learning and deep learning techniques: a review. MULTIMEDIA TOOLS AND APPLICATIONS 2023. [DOI: 10.1007/s11042-023-16520-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 05/12/2023] [Accepted: 08/13/2023] [Indexed: 09/16/2023] [Imported: 09/16/2023]
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Rai HM, Singh MK, Mishra AN, Solanki A. Hydroponic Farming as a Contemporary, Dependable, and Efficient Agricultural System: Overview. PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON INFORMATION MANAGEMENT AND MACHINE INTELLIGENCE 2023:141-147. [DOI: 10.1007/978-981-19-2065-3_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023] [Imported: 08/09/2023]
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Rai HM, Chatterjee K, Dashkevych S. The prediction of cardiac abnormality and enhancement in minority class accuracy from imbalanced ECG signals using modified deep neural network models. Comput Biol Med 2022; 150:106142. [PMID: 36182760 DOI: 10.1016/j.compbiomed.2022.106142] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/04/2022] [Accepted: 09/18/2022] [Indexed: 11/03/2022] [Imported: 08/08/2023]
Abstract
Cardiovascular disease (CVD) is the most fatal disease in the world, so its accurate and automated detection in the early stages will certainly support the medical expert in timely diagnosis and treatment, which can save many lives. Many types of research have been carried out in this regard, but due to the problem of data imbalance in the medical and health care sector, it may not provide the desired results in all aspects. To overcome this problem, a sequential ensemble technique has been proposed that detects 6 types of cardiac arrhythmias on large ECG imbalanced datasets, and the data imbalanced issue of the ECG dataset has been addressed by using a hybrid data resampling technique called "Synthetically Minority Oversampling Technique and Tomek Link (SMOTE + Tomek)". The sequential ensemble technique employs two distinct deep learning models: Convolutional Neural Network (CNN) and a hybrid model, CNN with Long Short-Term Memory Network (CNN-LSTM). The two standard datasets "MIT-BIH arrhythmias database" (MITDB) and "PTB diagnostic database" (PTBDB) are combined and extracted 23, 998 ECG beats for the model validation. In this work, the three models CNN, CNN-LSTM, and ensemble approach were tested on four kinds of ECG datasets: the original data (imbalanced), the data sampled using a random oversampled technique, data sampled using SMOTE, and the dataset resampled using SMOTE + Tomek algorithm. The overall highest accuracy was obtained of 99.02% on the SMOTE + Tomek sampled dataset by ensemble technique and the minority class accuracy result (Recall) is improved by 20% as compared to the imbalanced data.
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Rai HM, Goswami B, Majumdar S, Gupta K. COVID-19 TravelCover: Post-Lockdown Smart Transportation Management System. ASSESSING COVID-19 AND OTHER PANDEMICS AND EPIDEMICS USING COMPUTATIONAL MODELLING AND DATA ANALYSIS 2022:19-43. [DOI: 10.1007/978-3-030-79753-9_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023] [Imported: 08/09/2023]
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Rai HM, Chauhan M, Sharma H, Bhardwaj N, Kumar L. AgriBot: Smart Autonomous Agriculture Robot for Multipurpose Farming Application Using IOT. LECTURE NOTES IN ELECTRICAL ENGINEERING 2022:491-503. [DOI: 10.1007/978-981-19-0284-0_36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023] [Imported: 08/09/2023]
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Rai HM, Chatterjee K. 2D MRI image analysis and brain tumor detection using deep learning CNN model LeU-Net. MULTIMEDIA TOOLS AND APPLICATIONS 2021; 80:36111-36141. [DOI: 10.1007/s11042-021-11504-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 07/03/2021] [Accepted: 08/19/2021] [Indexed: 08/08/2023] [Imported: 08/08/2023]
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Ma S, Chen J, Zhang Y, Shrivastava A, Mohan H. Cloud based Resource Scheduling Methodology for Data-Intensive Smart Cities and Industrial Applications. SCALABLE COMPUTING: PRACTICE AND EXPERIENCE 2021; 22. [DOI: 10.12694/scpe.v22i2.1899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023] [Imported: 08/09/2023]
Abstract
For the data-intensive applications, resource planning and scheduling has become an important part for smart cities. The cloud computing techniques are being used for planning and scheduling of resources in data-intensive applications. The regular methodologies being used are adequately successful for giving the asset allotment yet they do not provide time effectiveness during task execution. This article presents an effective and time prioritization based smart resource management platform employing the Cuckoo Search based Optimized Resource Allocation (CSO-RA) methodology. The opensource JStorm platform is utilized for dynamic asset planning while using big data analytics and the outcomes of the experimentation are observed using various assessment parameters. The proposed (CSO-RA) system is compared with the current methodologies like particle swarm optimization (PSO), ant colony optimization (ACO) and genetic algorithm (GA) based optimization methodologies and the viability of the proposed framework is established. The percentage of optimality observed for CSO-RA algorithm is 97\% and overall resource deployment rate of 28\% is achieved using CSO-RA method which is comparatively much better than PSO, GA and ACO conventional algorithms. Feasible outcomes are obtained by using the CSO-RA methodology for cloud computing based large scale optimization-based data intensive industrial applications.
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Wang X, Zhang X, Gong H, Jiang J, Rai HM. A flight control method for unmanned aerial vehicles based on vibration suppression. IET COLLABORATIVE INTELLIGENT MANUFACTURING 2021; 3:252-261. [DOI: 10.1049/cim2.12027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023] [Imported: 08/08/2023]
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Rai HM, Gupta D, Mishra S, Sharma H. Agri-Bot: IoT Based Unmanned Smart Vehicle for Multiple Agriculture Operation. 2021 INTERNATIONAL CONFERENCE ON SIMULATION, AUTOMATION & SMART MANUFACTURING (SASM) 2021. [DOI: 10.1109/sasm51857.2021.9841182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023] [Imported: 08/09/2023]
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Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02696-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] [Imported: 08/08/2023]
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Rai HM, Chatterjee K, Dashkevich S. Automatic and accurate abnormality detection from brain MR images using a novel hybrid UnetResNext-50 deep CNN model. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102477] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] [Imported: 08/08/2023]
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Rai HM, Chatterjee K, Nayyar A. Automatic Segmentation and Classification of Brain Tumor from MR Images Using DWT-RBFNN. STUDIES IN BIG DATA 2021:215-243. [DOI: 10.1007/978-3-030-75657-4_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023] [Imported: 08/09/2023]
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Gupta S, Sharmila, Rai HM. IoT-Based Automatic Irrigation System Using Robotic Vehicle. ALGORITHMS FOR INTELLIGENT SYSTEMS 2021:669-677. [DOI: 10.1007/978-981-15-4936-6_73] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023] [Imported: 08/09/2023]
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Rai HM, Chatterjee K, Dubey A, Srivastava P. Myocardial Infarction Detection Using Deep Learning and Ensemble Technique from ECG Signals. PROCEEDINGS OF SECOND INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATIONS, AND CYBER-SECURITY 2021:717-730. [DOI: 10.1007/978-981-16-0733-2_51] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023] [Imported: 08/09/2023]
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Rai HM, Chatterjee K, Gupta D, Srivastava P. Tumor Detection from Brain Magnetic Resonance Images Using MRDWTA-RBFNNC. LECTURE NOTES IN NETWORKS AND SYSTEMS 2021:267-278. [DOI: 10.1007/978-981-15-9689-6_30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023] [Imported: 08/09/2023]
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Rai HM, Chatterjee K. Detection of brain abnormality by a novel Lu-Net deep neural CNN model from MR images. MACHINE LEARNING WITH APPLICATIONS 2020. [DOI: 10.1016/j.mlwa.2020.100004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] [Imported: 08/09/2023] Open
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Rai HM, Chatterjee K, Mukherjee C. Hybrid CNN-LSTM model for automatic prediction of cardiac arrhythmias from ECG big data. 2020 IEEE 7TH UTTAR PRADESH SECTION INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS AND COMPUTER ENGINEERING (UPCON) 2020. [DOI: 10.1109/upcon50219.2020.9376450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023] [Imported: 08/09/2023]
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