A multi model ensemble based deep convolution neural network structure for detection of COVID19

Deb, Sagar Deep; Jha, Rajib Kumar; Jha, Kamlesh; Tripathi, Prem Siddharth

doi:10.1016/j.bspc.2021.103126

Cited by 39 publications

(24 citation statements)

References 26 publications

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“…B. ensemble learning-based classification models: Ensemble learning is a training concept of constructing multiple basic classifiers and ensembling them into a more powerful classifier to make the final decision [26,27], which is widely used in classification tasks. One mode of ensemble learning is to generate the prediction function in parallel.…”

Section: Related Workmentioning

confidence: 99%

AdaD-FNN for Chest CT-Based COVID-19 Diagnosis

Yao

Zhu

Cheng

et al. 2023

IEEE Trans. Emerg. Top. Comput. Intell.

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Coronavirus disease 2019 (COVID-19) generated a global public health emergency since December 2019, causing huge economic losses. To help radiologists strengthen their recognition of COVID-19 cases, we developed a computer-aided diagnosis system based on deep learning to automatically classify chest computed tomography-based COVID-19, Tuberculosis, and healthy control subjects. Our novel classification model AdaD-FNN sequentially transfers the trained knowledge of an FNN estimator to the next FNN estimator while updating the weights of the samples in the training set with a decaying learning rate. This model inhibits the network from remembering the noisy information and improves the learning of complex patterns in the hard-to-identify samples. Moreover, we designed a novel image preprocessing model F-U2MNet-C by enhancing the image features using fuzzy stacking and eliminating the interference factors using U2MNet segmentation. Extensive experiments are conducted on four publicly available datasets namely, TLDCA, UCSD-Al4H, SARS-CoV-2, TCIA, and the obtained classification accuracies are 99.52%, 92.96%, 97.86%, 91.97%. Our novel system gives out compelling performance for assisting COVID-19 detection when compared with 22 state-of-the-art methods. We hope to help link together biomedical research and artificial intelligence and to assist the diagnosis of doctors, radiologists, and inspectors at each epidemic prevention site in the real world.

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Section: Related Workmentioning

confidence: 99%

AdaD-FNN for Chest CT-Based COVID-19 Diagnosis

Yao

Zhu

Cheng

et al. 2023

IEEE Trans. Emerg. Top. Comput. Intell.

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“…The authors obtained a maximum accuracy of 96.6% using a combination of VGG16 and binary robust invariant scalable key-points algorithm. Deb et al [102] proposed a multi-model deep CNN ensemble architecture for the classification of CXRs into binary (COVID-19 and non-COVID-19) and three-class (COVID-19, pneumonia, and normal). The authors obtained accuracies of 98.58% for binary and 93.48% for the three-class experiment.…”

Section: Related Workmentioning

confidence: 99%

Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models

et al. 2022

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Background and Motivation: The novel coronavirus causing COVID-19 is exceptionally contagious, highly mutative, decimating human health and life, as well as the global economy, by consistent evolution of new pernicious variants and outbreaks. The reverse transcriptase polymerase chain reaction currently used for diagnosis has major limitations. Furthermore, the multiclass lung classification X-ray systems having viral, bacterial, and tubercular classes—including COVID-19—are not reliable. Thus, there is a need for a robust, fast, cost-effective, and easily available diagnostic method. Method: Artificial intelligence (AI) has been shown to revolutionize all walks of life, particularly medical imaging. This study proposes a deep learning AI-based automatic multiclass detection and classification of pneumonia from chest X-ray images that are readily available and highly cost-effective. The study has designed and applied seven highly efficient pre-trained convolutional neural networks—namely, VGG16, VGG19, DenseNet201, Xception, InceptionV3, NasnetMobile, and ResNet152—for classification of up to five classes of pneumonia. Results: The database consisted of 18,603 scans with two, three, and five classes. The best results were using DenseNet201, VGG16, and VGG16, respectively having accuracies of 99.84%, 96.7%, 92.67%; sensitivity of 99.84%, 96.63%, 92.70%; specificity of 99.84, 96.63%, 92.41%; and AUC of 1.0, 0.97, 0.92 (p < 0.0001 for all), respectively. Our system outperformed existing methods by 1.2% for the five-class model. The online system takes <1 s while demonstrating reliability and stability. Conclusions: Deep learning AI is a powerful paradigm for multiclass pneumonia classification.

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“…Classifiers may be Support Vector Machine (SVM), SoftMax, Decision Trees, or Naïve Bayes Classifiers. Voting scheme [ 127 , 128 ], bagging [ 129 ], boosting [ 130 , 131 ], and stacking [ 132 , 133 ] are the most commonly used ensemble learning algorithms.…”

Section: The Taxonomy Of State-of-the-art Work On Thoracic Diseases D...mentioning

confidence: 99%

A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images

et al. 2022

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Thoracic diseases refer to disorders that affect the lungs, heart, and other parts of the rib cage, such as pneumonia, novel coronavirus disease (COVID-19), tuberculosis, cardiomegaly, and fracture. Millions of people die every year from thoracic diseases. Therefore, early detection of these diseases is essential and can save many lives. Earlier, only highly experienced radiologists examined thoracic diseases, but recent developments in image processing and deep learning techniques are opening the door for the automated detection of these diseases. In this paper, we present a comprehensive review including: types of thoracic diseases; examination types of thoracic images; image pre-processing; models of deep learning applied to the detection of thoracic diseases (e.g., pneumonia, COVID-19, edema, fibrosis, tuberculosis, chronic obstructive pulmonary disease (COPD), and lung cancer); transfer learning background knowledge; ensemble learning; and future initiatives for improving the efficacy of deep learning models in applications that detect thoracic diseases. Through this survey paper, researchers may be able to gain an overall and systematic knowledge of deep learning applications in medical thoracic images. The review investigates a performance comparison of various models and a comparison of various datasets.

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A multi model ensemble based deep convolution neural network structure for detection of COVID19

Cited by 39 publications

References 26 publications

AdaD-FNN for Chest CT-Based COVID-19 Diagnosis

AdaD-FNN for Chest CT-Based COVID-19 Diagnosis

Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models

A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images

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