CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection

Aslan, Muhammet Fatih; Ünlerşen, Muhammed Fahri; Sabancı, Kadir; Durdu, Akif

doi:10.1016/j.asoc.2020.106912

Cited by 276 publications

(195 citation statements)

References 46 publications

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“…1 BiLSTM Principle GR data can be regarded as the time series changing with the depth, and its shape classification depends on the previous output and current input. Assuming that a given GR sequence [12][13][14] can model each sequence forward and backward at the same time because each tag encoding contains contextual information from the past and the future, and it can better represent the long-term dependence on time series data in a richer way. Each layer of the BiLSTM network is composed of a single LSTM unit propagating forward and backward (Fig 2).…”

Section: Bilstm Principlementioning

confidence: 99%

Intelligent Identification Method of Sedimentary Microfacies Based on DMC-BiLSTM

Luo¹,

Zhou²,

Yx³

et al. 2021

Preprint

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Sedimentary microfacies division is the basis of oil and gas exploration research. The traditional sedimentary microfacies division mainly depends on human experience, which is greatly influenced by human factor and is low in efficiency. Although deep learning has its advantage in solving complex nonlinear problems, there is no effective deep learning method to solve sedimentary microfacies division so far. Therefore, this paper proposes a deep learning method based on DMC-BiLSTM for intelligent division of well-logging—sedimentary microfacies. First, the original curve is reconstructed multi-dimensionally by trend decomposition and median filtering, and spatio-temporal correlation clustering features are extracted from the reconstructed matrix by Kmeans. Then, taking reconstructed features, original curve features and clustering features as input, the prediction types of sedimentary microfacies at current depth are obtained based on BiLSTM. Experimental results show that this method can effectively classify sedimentary microfacies with its recognition efficiency reaching 96.84%.

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Section: Bilstm Principlementioning

confidence: 99%

Intelligent Identification Method of Sedimentary Microfacies Based on DMC-BiLSTM

Luo¹,

Zhou²,

Yx³

et al. 2021

Preprint

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“…However, in this paper, we will focus on applying machine learning techniques for the detection of COVID-19. In order to automate the task of detecting various lung abnormalities, several researchers are applying deep learning techniques to identify the affected regions in a CXR image [10] , [11] , [12] , [13] . Recently, the use of some well-known deep neural networks namely ImageNet (also known as ’AlexNet’) [14] , VGGNet [15] , GoogLeNet [16] , ResNet [17] , and their variations has been explored for identification of COVID-19 using CXR images [18] , [19] , [20] , [21] , [22] , [23] .…”

Section: Introductionmentioning

confidence: 99%

“…Aslan et al. [12] proposed two deep learning architectures which takes segmented lung area as an input and yielded better results with hybrid architecture that comprise pretrained Alexnet architecture followed by additional BiLSTM (Bidirectional Long Short-Term Memory) layer leveraged for identification of sequential and temporal properties. They experimented on dataset that comprise only 219 samples of COVID-19 and achieved an accuracy of 98.70%.…”

Section: Introductionmentioning

confidence: 99%

“…A deep learning based model’s performance depends on the amount of data used for training the model. However, most of the above cited works used a small number of COVID-19 images [12] , [13] , [18] , [27] , [30] , [34] . Another important aspect, validation on an independent cohort to establish the efficacy of a model, is missing from several works [13] , [18] , [19] , [20] , [27] , [34] .…”

Section: Introductionmentioning

confidence: 99%

“…Another important aspect, validation on an independent cohort to establish the efficacy of a model, is missing from several works [13] , [18] , [19] , [20] , [27] , [34] . Again, interpretability of results based on localizations used to mark the affected lung regions, a crucial issue in the medical domain, has not been properly addressed [12] , [13] , [18] , [33] . To address the above short comings, the proposed framework uses a balanced data set that comprise 520 COVID-19 patients’ CXR images and an equal number of normal and pneumonia CXR images.…”

Section: Introductionmentioning

confidence: 99%

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Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images

Rajpal

Lakhyani²,

Singh

et al. 2021

Chaos, Solitons & Fractals

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Coronaviruses are a family of viruses that majorly cause respiratory disorders in humans. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus that causes the coronavirus disease 2019 (COVID-19). WHO has identified COVID-19 as a pandemic as it has spread across the globe due to its highly contagious nature. For early diagnosis of COVID-19, the reverse transcription-polymerase chain reaction (RT-PCR) test is commonly done. However, it suffers from a high false-negative rate of up to 67% if the test is done during the first five days of exposure. As an alternative, research on the efficacy of deep learning techniques employed in the identification of COVID-19 disease using chest X-ray images is intensely pursued. As pneumonia and COVID-19 exhibit similar/ overlapping symptoms and affect the human lungs, a distinction between the chest X-ray images of pneumonia patients and COVID-19 patients becomes challenging. In this work, we have modeled the COVID-19 classification problem as a multiclass classification problem involving three classes, namely COVID-19, pneumonia, and normal. We have proposed a novel classification framework which combines a set of handpicked features with those obtained from a deep convolutional neural network. The proposed framework comprises of three modules. In the first module, we exploit the strength of transfer learning using ResNet-50 for training the network on a set of preprocessed images and obtain a vector of 2048 features. In the second module, we construct a pool of frequency and texture based 252 handpicked features that are further reduced to a set of 64 features using PCA. Subsequently, these are passed to a feed forward neural network to obtain a set of 16 features. The third module concatenates the features obtained from first and second modules, and passes them to a dense layer followed by the softmax layer to yield the desired classification model. We have used chest X-ray images of COVID-19 patients from four independent publicly available repositories, in addition to images from the Mendeley and Kaggle Chest X-Ray Datasets for pneumonia and normal cases. To establish the efficacy of the proposed model, 10-fold cross-validation is carried out. The model generated an overall classification accuracy of 0.974 0.02 and a sensitivity of 0.987 0.05, 0.963 0.05, and 0.973 0.04 at 95% confidence interval for COVID-19, normal, and pneumonia classes, respectively. To ensure the effectiveness of the proposed model, it was validated using an independent Chest X-ray cohort and an overall classification accuracy of 0.979 was achieved. Comparison of the proposed framework with state-of-the-art methods reveal that the proposed framework outperforms others in terms of accuracy and sensitivity. Since interpretability of results is crucial in the medical domain, the gradient-based localizations are captured using Gradient-weighted Class Activation Mapping (Grad-CAM). In su...

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A deep and handcrafted features‐based framework for diagnosis of COVID‐19 from chest x‐ray images

Bozkurt

2021

Concurrency and Computation

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Automatic early diagnosis of COVID‐19 with computer‐aided tools is crucial for disease treatment and control. Radiology images of COVID‐19 and other lung diseases like bacterial pneumonia, viral pneumonia have common features. Thus, this similarity makes it difficult for radiologists to detect COVID‐19 cases. A reliable method for classifying non‐COVID‐19 and COVID‐19 chest x‐ray images could be useful to reduce triage process and diagnose. In this study, we develop an original framework (HANDEFU) that supports handcrafted, deep, and fusion‐based feature extraction techniques for feature engineering. The user interactively builds any model by selecting feature extraction technique and classification method through the framework. Any feature extraction technique and model could then be added dynamically to the library of software at a later time upon request. The novelty of this study is that image preprocessing and diverse feature extraction and classification techniques are assembled under an original framework. In this study, this framework is utilized for diagnosing COVID‐19 from chest x‐ray images on an open‐access dataset. All of the experimental results and performance evaluations on this dataset are performed with this software. In experimental studies, COVID‐19 prediction is performed by 27 different models through software. The superior performance with accuracy of 99.36% is obtained by LBP+SVM model.

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CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection

Cited by 276 publications

References 46 publications

Intelligent Identification Method of Sedimentary Microfacies Based on DMC-BiLSTM

Intelligent Identification Method of Sedimentary Microfacies Based on DMC-BiLSTM

Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images

A deep and handcrafted features‐based framework for diagnosis of COVID‐19 from chest x‐ray images

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