Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19

Kumar, Santosh; Gupta, Sachin Kumar; Kumar, Vaibhav; Kumar, Mithilesh; Chaube, Mithilesh Kumar; Naik, Nenavath Srinivas

doi:10.1016/j.compeleceng.2022.108396

Cited by 15 publications

(4 citation statements)

References 19 publications

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“…The analysis of the proposed model was compared with (Deer Hunting Optimization Algorithm) DHOA-ODEC [33] , (Chimp Optimization Algorithm) ChOA-ODEC [34] , (Sea Lion Optimization) SLnO-ODEC [35] , and CMBO-ODEC [30] . On the second hand, different classifiers like CNN [18] , DNN [31] , LSTM [20] , RBF [32] , and Ensemble [36] were taken.…”

Section: Resultsmentioning

confidence: 99%

Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19

Awais

Bhuva

et al. 2023

Biomedical Signal Processing and Control

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Section: Resultsmentioning

confidence: 99%

Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19

Awais

Bhuva

et al. 2023

Biomedical Signal Processing and Control

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“…It is a combination of the DE-ANN algorithm and Fuzzy c-Means (FCM) clustering for skin cancer detection. The ensemble multimodal deep learning model developed by Kumar et al [183] utilizes Uniform-Net and convolutional neural network (CNN) to extract distinguishing features for coronavirus disease (COVID-19) patient diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Biological Network Mining

Yue

Yan

Guo

et al. 2023

Biostatistics Research

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In this survey, we explore the latest methods and trends in constructing and mining biological networks. We delve into cutting-edge techniques such as weighted gene co-expression network analysis (WGCNA), step-level differential response (SLDR), Biomedical Entity Expansion, Ranking and Explorations (BEERE), Weighted In-Network Node Expansion and Ranking (WINNER), and Weighted In-Path Edge Ranking (WIPER) from the Bioinformatics community, as well as breakthroughs in graph mining methods like parallel subgraph mining systems, temporal graph algorithms, and deep learning. To ensure a solid foundation, we provide an introductory-level overview of six well-established network types in systems biology. In addition, we offer a concise and accessible overview of strategies for network construction, including gene co-expression networks (GCNs), gene regulatory networks (GRNs), and literature-mined biomedical networks. We explain biological network mining in interdisciplinary domains, catering to both biomedical researchers and data mining experts. Our goal is to provide a comprehensive guide that doesn't require a significant time investment. We believe that these current trends will help readers become familiar with the topic and the practical applications of these tools in real-world studies.

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“…It achieved an accuracy of 0.86 for 400 training and validation and 200 tests, better than single-channel detection (either acetic acid or Lugol's iodine cervigram). There is more related work available in clinical practices [42][43][44][45][46] thanks to advances in computer vision methods. Some of these are observed as competent in performing the same or even better as the pathologists on medical data.…”

Section: Related Workmentioning

confidence: 99%

HDFCN: A Robust Hybrid Deep Network Based on Feature Concatenation for Cervical Cancer Diagnosis on WSI Pap Smear Slides

Chauhan

Singh²,

Kumar

et al. 2023

BioMed Research International

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Cervical cancer is a critical imperilment to a female’s health due to its malignancy and fatality rate. The disease can be thoroughly cured by locating and treating the infected tissues in the preliminary phase. The traditional practice for screening cervical cancer is the examination of cervix tissues using the Papanicolaou (Pap) test. Manual inspection of pap smears involves false-negative outcomes due to human error even in the presence of the infected sample. Automated computer vision diagnosis revamps this obstacle and plays a substantial role in screening abnormal tissues affected due to cervical cancer. Here, in this paper, we propose a hybrid deep feature concatenated network (HDFCN) following two-step data augmentation to detect cervical cancer for binary and multiclass classification on the Pap smear images. This network carries out the classification of malignant samples for whole slide images (WSI) of the openly accessible SIPaKMeD database by utilizing the concatenation of features extracted from the fine-tuning of the deep learning (DL) models, namely, VGG-16, ResNet-152, and DenseNet-169, pretrained on the ImageNet dataset. The performance outcomes of the proposed model are compared with the individual performances of the aforementioned DL networks using transfer learning (TL). Our proposed model achieved an accuracy of 97.45% and 99.29% for 5-class and 2-class classifications, respectively. Additionally, the experiment is performed to classify liquid-based cytology (LBC) WSI data containing pap smear images.

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Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19

Cited by 15 publications

References 19 publications

Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19

Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19

Biological Network Mining

HDFCN: A Robust Hybrid Deep Network Based on Feature Concatenation for Cervical Cancer Diagnosis on WSI Pap Smear Slides

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