DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays

Malik, Hassaan; Naeem, Ahmad; Loh, Woong-Kee

doi:10.3390/s23020743

Cited by 44 publications

(20 citation statements)

References 76 publications

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“…We have offered a comprehensive evaluation of the usefulness of the proposed structure, examining its performance both with and without the concentration key point elements being taken into consideration. The findings of the experiments reveal that the DCNN models work more effectively than the simple CNN model consistently [80]. The results of this study substantially indicate the effectiveness of the suggested method (ORB & VGG-19 with softmax) in classifying ten different chest diseases using CXR images.…”

Section: G Discussionmentioning

confidence: 52%

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

et al. 2023

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With the continuing global pandemic of coronavirus (COVID-19) sickness, it is critical to seek diagnostic approaches that are both effective and rapid to limit the number of people infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The results of recent research suggest that radiological images include important information related to COVID-19 and other chest diseases. As a result, the use of deep learning (DL) to assist in the automated diagnosis of chest diseases may prove useful as a diagnostic tool in the future. In this study, we propose a novel fusion model of hand-crafted features with deep convolutional neural networks (DCNNs) for classifying ten different chest diseases such as COVID-19, lung cancer (LC), atelectasis (ATE), consolidation lung (COL), tuberculosis (TB), pneumothorax (PNET), edema (EDE), pneumonia (PNEU), pleural thickening (PLT), and normal using chest X-rays (CXR). The method that has been suggested is split down into three distinct parts. The first step involves utilizing the Info-MGAN network to perform segmentation on the raw CXR data to construct lung images of ten different chest diseases. In the second step, the segmented lung images are fed into a novel pipeline that extracts discriminatory features by using hand-crafted techniques such as SURF and ORB, and then these extracted features are fused to the trained DCNNs. At last, various machine learning (ML) models have been used as the last layer of the DCNN models for the classification of chest diseases. Comparison is made between the performance of various proposed architectures for classification, all of which integrate DCNNs, key point extraction methods, and ML models. We were able to attain a classification accuracy of 98.20% for testing by utilizing the VGG-19 model with a softmax layer in conjunction with the ORB technique. Screening for COVID-19 and other lung ailments can be accomplished using the method that has been proposed. The robustness of the model was further confirmed by statistical analyses of the datasets using McNemar's and ANOVA tests respectively.

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Section: G Discussionmentioning

confidence: 52%

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

et al. 2023

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“…In order to assess the classification performance of the BFLPD framework for pandemic disease diagnosis in smart cities, we compared it against state-of-the-art models [24][25][26][27][28][29]. The results of this comparative analysis are presented in Table 2, showcasing the performance metrics obtained from the evaluation.…”

Section: Resultsmentioning

confidence: 99%

Towards Efficient and Trustworthy Pandemic Diagnosis in Smart Cities: A Blockchain-Based Federated Learning Approach

et al. 2023

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In the aftermath of the COVID-19 pandemic, the need for efficient and reliable disease diagnosis in smart cities has become increasingly serious. In this study, we introduce a novel blockchain-based federated learning framework tailored specifically for the diagnosis of pandemic diseases in smart cities, called BFLPD, with a focus on COVID-19 as a case study. The proposed BFLPD takes advantage of the decentralized nature of blockchain technology to design collaborative intelligence for automated diagnosis without violating trustworthiness metrics, such as privacy, security, and data sharing, which are encountered in healthcare systems of smart cities. Cheon–Kim–Kim–Song (CKKS) encryption is intelligently redesigned in BFLPD to ensure the secure sharing of learning updates during the training process. The proposed BFLPD presents a decentralized secure aggregation method that safeguards the integrity of the global model against adversarial attacks, thereby improving the overall efficiency and trustworthiness of our system. Extensive experiments and evaluations using a case study of COVID-19 ultrasound data demonstrate that BFLPD can reliably improve diagnostic accuracy while preserving data privacy, making it a promising tool with which smart cities can enhance their pandemic disease diagnosis capabilities.

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“…Deep neural networks [3, were a part of the pre-trained image classifier; however, the final convolutional layers of these networks resulted in a loss of the spatial resolution of the feature maps, which in turn restricted the classification capabilities of these models. In addition, the large number of neurons coupled to the input resulted in an insufficiently large filter size for these networks [51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70]73,74]. Because of this, the network could overlook significant traits as soon as they were discovered, which is a problem in how the network was designed.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Classification of Abrasion and Ischemic Diabetic Foot Sores Using Camera-Captured Images

Khalil,

Naeem,

Naqvi

et al. 2023

Mathematics

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Diabetic foot sores (DFS) are serious diabetic complications. The patient’s weakened neurological system damages the tissues of the foot’s skin, which results in amputation. This study aims to validate and deploy a deep learning-based system for the automatic classification of abrasion foot sores (AFS) and ischemic diabetic foot sores (DFS). We proposed a novel model combining convolutional neural network (CNN) capabilities with Vgg-19. The proposed method utilized two benchmark datasets to classify AFS and DFS from the patient’s foot. A data augmentation technique was used to enhance the accuracy of the training. Moreover, image segmentation was performed using UNet++. We tested and evaluated the proposed model’s classification performance against two well-known pre-trained classifiers, Inceptionv3 and MobileNet. The proposed model classified AFS and ischemia DFS images with an accuracy of 99.05%, precision of 98.99%, recall of 99.01%, MCC of 0.9801, and f1 score of 99.04%. Furthermore, the results of statistical evaluations using ANOVA and Friedman tests revealed that the proposed model exhibited a remarkable performance. The proposed model achieved an excellent performance that assist medical professionals in identifying foot ulcers.

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DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays

Cited by 44 publications

References 76 publications

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

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

Towards Efficient and Trustworthy Pandemic Diagnosis in Smart Cities: A Blockchain-Based Federated Learning Approach

Deep Learning-Based Classification of Abrasion and Ischemic Diabetic Foot Sores Using Camera-Captured Images

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