A Novel Fusion Model of Hand-Crafted Features With Deep Convolutional Neural Networks for Classification of Several Chest Diseases Using X-Ray Images

Malik, Hassaan; Anees, Tayyaba; Chaudhry, Muhammad Umar; Goňo, Radomír; Jasiński, Michał; Leonowicz, Zbigniew; Bernat, Petr

doi:10.1109/access.2023.3267492

Cited by 7 publications

(5 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This demonstrates that the suggested model was statistically distinct from the other contributing models since it combined more information from the base classifiers and produced better predictions. In this section, we evaluate the suggested DCDD_Net model with previous research [82][83][84][85][86][87]. In comparison to prior SOTA studies, Table 8 provides an in-depth analysis of the proposed DCDD_Net model in the context of numerous performance assessment criteria, including accuracy, recall, and F1-score.…”

Section: Discussionmentioning

confidence: 99%

“…Cross-sectional images are produced using a CT scan, which combines several X-ray images collected at various angles. Scalograms represent the actual frequencies of a wave's continuous wavelet transform (CWT) factors [82][83][84][85][86][87]. Cough signals utilize CWT to convey data from the time domain to the frequency domain, as demonstrated in Figure 3.…”

Section: Discussionmentioning

confidence: 99%

“…They achieved 90.39% accuracy, a recall of 90.13%, and 92.26% precision. A framework for the automatic detection of COVID-19 employing chest CT scan pictures and DL-based algorithms was developed by Gupta et al [85]. Using DarkNet 19, the greatest accuracy in classification of 94.91% was obtained.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images

Malik,

Anees,

Al-Shamaylehs

et al. 2023

Diagnostics

Self Cite

View full text Add to dashboard Cite

Chest disease refers to a variety of lung disorders, including lung cancer (LC), COVID-19, pneumonia (PNEU), tuberculosis (TB), and numerous other respiratory disorders. The symptoms (i.e., fever, cough, sore throat, etc.) of these chest diseases are similar, which might mislead radiologists and health experts when classifying chest diseases. Chest X-rays (CXR), cough sounds, and computed tomography (CT) scans are utilized by researchers and doctors to identify chest diseases such as LC, COVID-19, PNEU, and TB. The objective of the work is to identify nine different types of chest diseases, including COVID-19, edema (EDE), LC, PNEU, pneumothorax (PNEUTH), normal, atelectasis (ATE), and consolidation lung (COL). Therefore, we designed a novel deep learning (DL)-based chest disease detection network (DCDD_Net) that uses a CXR, CT scans, and cough sound images for the identification of nine different types of chest diseases. The scalogram method is used to convert the cough sounds into an image. Before training the proposed DCDD_Net model, the borderline (BL) SMOTE is applied to balance the CXR, CT scans, and cough sound images of nine chest diseases. The proposed DCDD_Net model is trained and evaluated on 20 publicly available benchmark chest disease datasets of CXR, CT scan, and cough sound images. The classification performance of the DCDD_Net is compared with four baseline models, i.e., InceptionResNet-V2, EfficientNet-B0, DenseNet-201, and Xception, as well as state-of-the-art (SOTA) classifiers. The DCDD_Net achieved an accuracy of 96.67%, a precision of 96.82%, a recall of 95.76%, an F1-score of 95.61%, and an area under the curve (AUC) of 99.43%. The results reveal that DCDD_Net outperformed the other four baseline models in terms of many performance evaluation metrics. Thus, the proposed DCDD_Net model can provide significant assistance to radiologists and medical experts. Additionally, the proposed model was also shown to be resilient by statistical evaluations of the datasets using McNemar and ANOVA tests.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images

Malik,

Anees,

Al-Shamaylehs

et al. 2023

Diagnostics

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the above equation, A means the number of neurons in a single layer. Here, the activation function is GELU, and the principle is shown as equation (7). Where, tanh means the hyperbolic tangent function GELU(s) = s 2 (1 + tanh( 2/π (s + 0.044 715s 3 )).…”

Section: Qad Modulementioning

confidence: 99%

“…Although the pre-diagnosis methods for respiratory diseases are constantly improved, they still face certain challenges [3][4][5][6][7][8]. Firstly, although the classical pre-diagnosis method can improve the consultation efficiency of patients, it also requires the participation of a large number of human doctors, and it is difficult to effectively reduce the burden on relevant doctors.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence can dynamically adjust strategies for auxiliary diagnosing respiratory diseases and analyzing potential pathological relationships

Zhang,

Chen,

Liu

2023

J. Breath Res.

View full text Add to dashboard Cite

Respiratory diseases are one of the leading causes of human death and exacerbate the global burden of non-communicable diseases. Finding a method to assist clinicians pre-diagnose these diseases is an urgent task. Existing artificial intelligence-based methods can improve the clinical diagnosis efficiency, but still face challenges. For example, the lack of interpretability, the problem of information redundancy or missing caused by only using static data, the difficulty of model to learn the interdependence between features, and the performance of model is limited by sparse datasets, etc. To alleviate these problems, we propose a novel RQPA-Net. It consists of Q&A diagnosis module (QAD) and pathological inference module (PI). The QAD is responsible for interacting with patients, adjusting inquiry strategies dynamically and collecting effective information for disease diagnosis. The designed multi-subspace network can alleviate the problem that classical method is difficult to understand the interdependence between features. The deep reinforcement learning designed also can alleviate the problem of classical methods lack of interpretability. The PI is responsible for reasoning potential pathological relationships between diseases or symptoms based on existing knowledge. Through integrating the advantages of deep learning and reinforcement learning techniques, PI can handle sparse datasets. Finally, for auxiliary diagnosis, the model achieves 0.9780±0.0002 Recall, 0.9778±0.0003 Acc, 0.9779±0.0003 Precision and 0.9780±0.0003 F1-score on the test set. In terms of assisting pathological analysis, compared with the end-to-end model, our model achieves higher comprehensive performance on different tasks and datasets with different degrees of sparsity. Even in sparse datasets, it can effectively infer potential associations between diseases or symptoms, and has higher potential clinical application. In this paper, we propose a novel network structure, which can not only assist doctors in diagnosing diseases, but also contribute to explore the potential disease mechanisms. It provides a new perspective for integrating AI technology and clinical practice.

show abstract

Multi-scale Lesion Feature Fusion and Location-Aware for Chest Multi-disease Detection

Yuan,

Liu,

Yang

et al. 2024

J Digit Imaging. Inform. med.

View full text Add to dashboard Cite

A Novel Fusion Model of Hand-Crafted Features With Deep Convolutional Neural Networks for Classification of Several Chest Diseases Using X-Ray Images

Cited by 7 publications

References 72 publications

Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images

Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images

Artificial intelligence can dynamically adjust strategies for auxiliary diagnosing respiratory diseases and analyzing potential pathological relationships

Multi-scale Lesion Feature Fusion and Location-Aware for Chest Multi-disease Detection

Contact Info

Product

Resources

About