Multimodal Convolutional Neural Networks for Detection of Covid-19 Using Chest X-Ray and CT Images

Ouahab, Abdelwhab

doi:10.3103/s1060992x21040044

Cited by 7 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Others have tuned the parameters of existing models to get their desired results. [25] Our Model COVID-CT-Net 98.75% 98.65% Yener et al [22] VGG16 93.00% 92.00% Anwar et al [35] EffiecientNet-B4 89.70% 89.60% Islam et al [39] LeNet-5 CNN 86.06% 87.00% Soares et al [33] Our Model COVID-CT-Net 98.54% 98.47% Wu et al [28] VGG16, ResNet 82.50% -Foysal et al [41] Ensembled CNN 96.00% 95.60% Panwar et al [56] Modified VGG19 95.00% 95.00% Maftouni et al [42] Our Model COVID-CT-Net 97.84% 97.80% Dhruv et al [57] InRFNet 96.00% 96.33% A. Ouahab [58] CNN 88.30% 88.50% Hartono at al. [59] LeNet-5 83.33% 84.89%…”

Section: Comparison With Other Methods and Resultsmentioning

confidence: 99%

A Fast and Reliable Approach for COVID-19 Detection from CT-Scan Images

Bin Zahir,

Azim,

Chy

et al. 2023

J. Inf. Syst. Eng. Bus. Intell.

View full text Add to dashboard Cite

Background: COVID-19 is a highly contagious respiratory disease with multiple mutant variants, an asymptotic nature in patients, and with potential to stay undetected in common tests, which makes it deadlier, more transmissible, and harder to detect. Regardless of variants, the COVID-19 infection shows several observable anomalies in the computed tomography (CT) scans of the lungs, even in the early stages of infection. A quick and reliable way of detecting COVID-19 is essential to manage the growing transmission of COVID-19 and save lives. Objective: This study focuses on developing a deep learning model that can be used as an auxiliary decision system to detect COVID-19 from chest CT-scan images quickly and effectively. Methods: In this research, we propose a MobileNet-based transfer learning model to detect COVID-19 in CT-scan images. To test the performance of our proposed model, we collect three publicly available COVID-19 CT-scan datasets and prepare another dataset by combining the collected datasets. We also implement a mobile application using the model trained on the combined dataset, which can be used as an auxiliary decision system for COVID-19 screening in real life. Results: Our proposed model achieves a promising accuracy of 96.14% on the combined dataset and accuracy of 98.75%, 98.54%, and 97.84% respectively in detecting COVID-19 samples on the collected datasets. It also outperforms other transfer learning models while having lower memory consumption, ensuring the best performance in both normal and low-powered, resource-constrained devices. Conclusion: We believe, the promising performance of our proposed method will facilitate its use as an auxiliary decision system to detect COVID-19 patients quickly and reliably. This will allow authorities to take immediate measures to limit COVID-19 transmission to prevent further casualties as well as accelerate the screening for COVID-19 while reducing the workload of medical personnel. Keywords: Auxiliary Decision System, COVID-19, CT Scan, Deep Learning, MobileNet, Transfer Learning

show abstract

Section: Comparison With Other Methods and Resultsmentioning

confidence: 99%

A Fast and Reliable Approach for COVID-19 Detection from CT-Scan Images

Bin Zahir,

Azim,

Chy

et al. 2023

J. Inf. Syst. Eng. Bus. Intell.

View full text Add to dashboard Cite

show abstract

“…In medical applications such as medical imaging and clinical data analysis, where multiple categories of data (e.g., images and patient records) must be jointly analyzed to make accurate diagnoses, a multimodal neural network is particularly advantageous. By integrating and simultaneously learning from multiple modalities, the network can make more informed decisions, leading to improved diagnosis An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification and treatment planning [32]. Figure 2 depicts the architectural components and connections of a multimodal neural network model.…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification

Hamida,

Lamrani,

Bouqentar

et al. 2024

Int. J. Onl. Eng.

View full text Add to dashboard Cite

In order to effectively treat skin diseases, an accurate and prompt diagnosis is required. In this article, a novel method for classifying skin disorders using a multimodal classifier is presented. The proposed classifier utilizes multiple information sources to enhance the accuracy of disease classification. It incorporates images of skin lesions and patient-specific data. The multimodal classifier simultaneously classifies diseases by combining image and structured data inputs. The effectiveness of the proposed classifier was evaluated using the ISIC 2018 dataset, which includes images and clinical data for seven categories of skin diseases. The results indicate that the proposed model outperforms conventional single-modal and single-task classifiers, achieving an accuracy of 98.66% for image classification and 94.40% for clinical data classification. In addition, we compare the performance of the proposed model with that of other methodologies, demonstrating its superiority. Despite yielding promising results, the proposed method has limitations in terms of data requirements and generalizability. Future research directions include incorporating additional information sources, investigating genetic data integration, and applying the method to various medical conditions. This study illustrates the potential of integrating multimodal techniques with transfer learning in deep neural networks to enhance the classification accuracy of cutaneous diseases.

show abstract

“…The multimodal features obtained are fused (early & joint) using concatenation operation and late fused using averaging. Naufal et al [ 29 ] and Ouahab et al [ 53 ] proposed CNN based feature extraction technique to detect Covid-19 from the two different modality images (CT + X-ray). The authors have applied joint fusion concatenation for integrating both the imaging features.…”

Section: Literature Reviewmentioning

confidence: 99%

Multimodal medical tensor fusion network-based DL framework for abnormality prediction from the radiology CXRs and clinical text reports

Shetty

Ananthanarayana

Mahale

2023

Multimed Tools Appl

View full text Add to dashboard Cite

Pulmonary disease is a commonly occurring abnormality throughout this world. The pulmonary diseases include Tuberculosis, Pneumothorax, Cardiomegaly, Pulmonary atelectasis, Pneumonia, etc. A timely prognosis of pulmonary disease is essential. Increasing progress in Deep Learning (DL) techniques has significantly impacted and contributed to the medical domain, specifically in leveraging medical imaging for analysis, prognosis, and therapeutic decisions for clinicians. Many contemporary DL strategies for radiology focus on a single modality of data utilizing imaging features without considering the clinical context that provides more valuable complementary information for clinically consistent prognostic decisions. Also, the selection of the best data fusion strategy is crucial when performing Machine Learning (ML) or DL operation on multimodal heterogeneous data. We investigated multimodal medical fusion strategies leveraging DL techniques to predict pulmonary abnormality from the heterogeneous radiology Chest X-Rays (CXRs) and clinical text reports. In this research, we have proposed two effective unimodal and multimodal subnetworks to predict pulmonary abnormality from the CXR and clinical reports. We have conducted a comprehensive analysis and compared the performance of unimodal and multimodal models. The proposed models were applied to standard augmented data and the synthetic data generated to check the model’s ability to predict from the new and unseen data. The proposed models were thoroughly assessed and examined against the publicly available Indiana university dataset and the data collected from the private medical hospital. The proposed multimodal models have given superior results compared to the unimodal models.

show abstract

Multimodal Convolutional Neural Networks for Detection of Covid-19 Using Chest X-Ray and CT Images

Cited by 7 publications

References 21 publications

A Fast and Reliable Approach for COVID-19 Detection from CT-Scan Images

A Fast and Reliable Approach for COVID-19 Detection from CT-Scan Images

An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification

Multimodal medical tensor fusion network-based DL framework for abnormality prediction from the radiology CXRs and clinical text reports

Contact Info

Product

Resources

About