A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis

Aslan, Muhammet Fatih

doi:10.1016/j.chemolab.2022.104695

Cited by 15 publications

(7 citation statements)

References 58 publications

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“…This curve was created by plotting the true positive rate against the false positive rate. For successful classification, the area under the curve (AUC) should be close to 1 [ 70 ]. As seen in Figure 6 , the AUC value was calculated as 1 for all curves.…”

Section: Resultsmentioning

confidence: 99%

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

2022

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Among the leading causes of mortality and morbidity in people are lung and colon cancers. They may develop concurrently in organs and negatively impact human life. If cancer is not diagnosed in its early stages, there is a great likelihood that it will spread to the two organs. The histopathological detection of such malignancies is one of the most crucial components of effective treatment. Although the process is lengthy and complex, deep learning (DL) techniques have made it feasible to complete it more quickly and accurately, enabling researchers to study a lot more patients in a short time period and for a lot less cost. Earlier studies relied on DL models that require great computational ability and resources. Most of them depended on individual DL models to extract features of high dimension or to perform diagnoses. However, in this study, a framework based on multiple lightweight DL models is proposed for the early detection of lung and colon cancers. The framework utilizes several transformation methods that perform feature reduction and provide a better representation of the data. In this context, histopathology scans are fed into the ShuffleNet, MobileNet, and SqueezeNet models. The number of deep features acquired from these models is subsequently reduced using principal component analysis (PCA) and fast Walsh–Hadamard transform (FHWT) techniques. Following that, discrete wavelet transform (DWT) is used to fuse the FWHT’s reduced features obtained from the three DL models. Additionally, the three DL models’ PCA features are concatenated. Finally, the diminished features as a result of PCA and FHWT-DWT reduction and fusion processes are fed to four distinct machine learning algorithms, reaching the highest accuracy of 99.6%. The results obtained using the proposed framework based on lightweight DL models show that it can distinguish lung and colon cancer variants with a lower number of features and less computational complexity compared to existing methods. They also prove that utilizing transformation methods to reduce features can offer a superior interpretation of the data, thus improving the diagnosis procedure.

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Section: Resultsmentioning

confidence: 99%

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

2022

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“…Perumal et al [ 16 ] used X-ray and CT images for lung disease classification, but their methods were tested on a small dataset and provided an accuracy lower than the proposed method. However, Aslan et al [ 13 ] reported a better method, since it not only had complex processes to deliver output but also used only chest X-ray images to classify COVID-19, pneumonia, and normal cases. Hamed et al [ 17 ] achieved high performance, but they only discussed binary classification (COVID or non-COVID) based on CT images to classify, showing that their method is non-flexible for input format and has less categorical ability than the proposed method, which can classify three lung disease categories.…”

Section: Discussionmentioning

confidence: 99%

“…The optimal method, which used gamma-corrected images and CheXNet, achieved an accuracy of 96.2% but the method can only classify images as COVID or non-COVID. Aslan [ 13 ] used two-step CNN methods to identify viral pneumonia, COVID-19, and normal cases from chest X-ray images. Initially, the DeepLabV3+ network used a chest X-ray dataset to semantically partition the lung sections in X-ray images, and image processing techniques, such as dilation, erosion, use of a Gaussian filter, and image thresholding, were used to improve the segmented output.…”

Section: Introductionmentioning

confidence: 99%

CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images

Kanjanasurat

Tenghongsakul

Purahong

et al. 2023

Sensors

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The 2019 coronavirus disease (COVID-19) has rapidly spread across the globe. It is crucial to identify positive cases as rapidly as humanely possible to provide appropriate treatment for patients and prevent the pandemic from spreading further. Both chest X-ray and computed tomography (CT) images are capable of accurately diagnosing COVID-19. To distinguish lung illnesses (i.e., COVID-19 and pneumonia) from normal cases using chest X-ray and CT images, we combined convolutional neural network (CNN) and recurrent neural network (RNN) models by replacing the fully connected layers of CNN with a version of RNN. In this framework, the attributes of CNNs were utilized to extract features and those of RNNs to calculate dependencies and classification base on extracted features. CNN models VGG19, ResNet152V2, and DenseNet121 were combined with long short-term memory (LSTM) and gated recurrent unit (GRU) RNN models, which are convenient to develop because these networks are all available as features on many platforms. The proposed method is evaluated using a large dataset totaling 16,210 X-ray and CT images (5252 COVID-19 images, 6154 pneumonia images, and 4804 normal images) were taken from several databases, which had various image sizes, brightness levels, and viewing angles. Their image quality was enhanced via normalization, gamma correction, and contrast-limited adaptive histogram equalization. The ResNet152V2 with GRU model achieved the best architecture with an accuracy of 93.37%, an F1 score of 93.54%, a precision of 93.73%, and a recall of 93.47%. From the experimental results, the proposed method is highly effective in distinguishing lung diseases. Furthermore, both CT and X-ray images can be used as input for classification, allowing for the rapid and easy detection of COVID-19.

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“…On a dataset comprised of COVID-19 X-ray images and normal, pneumonia, and other X-ray images, we normally evaluate the models. The imbalanced dataset and the lack of COVID-19 images in earlier research like [1], [15], [17], [19], [20], [22], [24], [25], and [27] are also key issues that have been resolved by this dataset. The COVID-ChestXray-15k dataset was compiled from 11 sources and contained 4420 COVID-19 pictures before data augmentation, 5000 images of normal chest X-rays, and 5000 images with pneumonia.…”

Section: Data Descriptionmentioning

confidence: 99%

“…AlexNet is used in [22] to extract features, and SVM is subsequently applied for classification. In [23], features are extracted using CNNs, and then they are classified using a variety of machine-learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Ensemble deep models for covid-19 pandemic classification using chest x-ray images via different fusion techniques

Menshawy

Eid

Abdel-Kader

2023

Int. J. Adv. Intell. Informatics

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A pandemic epidemic called the coronavirus (COVID-19) has already afflicted people all across the world. Radiologists can visually detect coronavirus infection using a chest X-ray. This study examines two methods for categorizing COVID-19 patients based on chest x-rays: pure deep learning and traditional machine learning. In the first model, three deep learning classifiers' decisions are combined using two distinct decision fusion strategies (majority voting and Bayes optimal). To enhance classification performance, the second model merges the ideas of decision and feature fusion. Using the fusion procedure, feature vectors from deep learning models generate a feature set. The classification metrics of conventional machine learning classifiers were then optimized using a voting classifier. The first proposed model performs better than the second model when it concerns diagnosing binary and multiclass classification. The first model obtains an AUC of 0.998 for multi-class classification and 0.9755 for binary classification. The second model obtains a binary classification AUC of 0.9563 and a multiclass classification AUC of 0.968. The suggested models perform better than both the standard learners and state-of-the-art and state-of-the-art methods.

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A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis

Cited by 15 publications

References 58 publications

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods

CNN–RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images

Ensemble deep models for covid-19 pandemic classification using chest x-ray images via different fusion techniques

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