COVID-19 and human development: An approach for classification of HDI with deep CNN

Kavuran, Gürkan; Gökhan, Şeyma; Yeroğlu, Celaleddin

doi:10.1016/j.bspc.2022.104499

Cited by 3 publications

(6 citation statements)

References 33 publications

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“…; d 5 ðjDjÞg. The pre-processed chest diseases dataset is divided into three categories such as training (Z Train ), validation (Z Val ), and testing (Z Test ) as discussed in Eq (37).…”

Section: Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…However, numerous studies [37][38][39][40] used cough sounds for the identification of several chest diseases such as COVID-19, tuberculosis, etc. Kavuran et al [37] conceived of a study that makes use of the DCNN model in conjunction with the continuous wavelet transform (CWT), and scalogram approaches were used for the depiction of COVID-19 anomalies. In addition, following the training and validation of the model that was presented, the feature vectors that were stored in the fc1000 layer of the network were drawn and provided as input to the SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

“…The classification of diseases has been altered as a result of deep learning (DL) models, which have opened up new doors for medical professionals [35][36][37][38][39][40][41][42][43][44]. Chest infections [45], the detection of cancer cells [46], the segmentation and identification of brain and breast tumors [47], and gene analysis [48,49] have been significantly improved as a result of medical systems partnering with convolutional neural networks (CNN).…”

Section: Introductionmentioning

confidence: 99%

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Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

Malik,

Anees

2024

PLoS ONE

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Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cancer (LC), consolidation lung (COL), and many more. When diagnosing chest disorders medical professionals may be thrown off by the overlapping symptoms (such as fever, cough, sore throat, etc.). Additionally, researchers and medical professionals make use of chest X-rays (CXR), cough sounds, and computed tomography (CT) scans to diagnose chest disorders. The present study aims to classify the nine different conditions of chest disorders, including COVID-19, LC, COL, atelectasis (ATE), tuberculosis (TB), pneumothorax (PNEUTH), edema (EDE), pneumonia (PNEU). Thus, we suggested four novel convolutional neural network (CNN) models that train distinct image-level representations for nine different chest disease classifications by extracting features from images. Furthermore, the proposed CNN employed several new approaches such as a max-pooling layer, batch normalization layers (BANL), dropout, rank-based average pooling (RBAP), and multiple-way data generation (MWDG). The scalogram method is utilized to transform the sounds of coughing into a visual representation. Before beginning to train the model that has been developed, the SMOTE approach is used to calibrate the CXR and CT scans as well as the cough sound images (CSI) of nine different chest disorders. The CXR, CT scan, and CSI used for training and evaluating the proposed model come from 24 publicly available benchmark chest illness datasets. The classification performance of the proposed model is compared with that of seven baseline models, namely Vgg-19, ResNet-101, ResNet-50, DenseNet-121, EfficientNetB0, DenseNet-201, and Inception-V3, in addition to state-of-the-art (SOTA) classifiers. The effectiveness of the proposed model is further demonstrated by the results of the ablation experiments. The proposed model was successful in achieving an accuracy of 99.01%, making it superior to both the baseline models and the SOTA classifiers. As a result, the proposed approach is capable of offering significant support to radiologists and other medical professionals.

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“…; d 5 ðjDjÞg. The pre-processed chest diseases dataset is divided into three categories such as training (Z Train ), validation (Z Val ), and testing (Z Test ) as discussed in Eq (37).…”

Section: Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

Malik,

Anees

2024

PLoS ONE

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show abstract

“…An encoder-decoder architecture consisting of CNN is predominant in the community of MDE tasks. CNN uses convolutional operations within a local receptive field and downsampling to extract hierarchical features from input images [8,9]. The lower layers have high resolution and a small receptive field, while the higher layers have low resolution and a large receptive field.…”

Section: Introductionmentioning

confidence: 99%

Attention Mechanism Used in Monocular Depth Estimation: An Overview

Li,

Wei,

Fan

2023

Applied Sciences

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Monocular depth estimation (MDE), as one of the fundamental tasks of computer vision, plays important roles in downstream applications such as virtual reality, 3D reconstruction, and robotic navigation. Convolutional neural networks (CNN)-based methods gained remarkable progress compared with traditional methods using visual cues. However, recent researches reveal that the performance of MDE using CNN could be degraded due to the local receptive field of CNN. To bridge the gap, various attention mechanisms were proposed to model the long-range dependency. Although reviews of MDE algorithms based on CNN were reported, a comprehensive outline of how attention boosts MDE performance is not explored yet. In this paper, we firstly categorize recent attention-related works into CNN-based, Transformer-based, and hybrid (CNN–Transformer-based) approaches in the light of how the attention mechanism impacts the extraction of global features. Secondly, we discuss the details and contributions of attention-based MDE methods published from 2020 to 2022. Then, we compare the performance of the typical attention-based methods. Finally, the challenges and trends of the attention mechanism used in MDE are discussed.

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International comparisons of COVID-19 pandemic management: What can be learned from activity analysis techniques?

Giménez,

Prior,

Thieme

et al. 2024

Omega

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COVID-19 and human development: An approach for classification of HDI with deep CNN

Cited by 3 publications

References 33 publications

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

Attention Mechanism Used in Monocular Depth Estimation: An Overview

International comparisons of COVID-19 pandemic management: What can be learned from activity analysis techniques?

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