Federated Learning Approach for Early Detection of Chest Lesion Caused by COVID-19 Infection Using Particle Swarm Optimization

Kandati, Dasaradharami Reddy; Gadekallu, Thippa Reddy

doi:10.3390/electronics12030710

Cited by 17 publications

(7 citation statements)

References 47 publications

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“…PSO suffers early convergence at the initial stages and consumes a lot of time. PSO is more suited for smaller dataset compared to larger dataset, since it took a long time and large space search [15].…”

Section: Resultsmentioning

confidence: 99%

Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization

Muliawan,

Angky,

Prasetyo

2023

E3S Web Conf.

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There has been a lot of recent study on age estimates utilizing different optimization techniques, architecture models, and diverse strategies with some variations. However, accuracy improvement in age estimation studies remains a challenge due to the inability of traditional approaches to effectively capture complex facial features and variations. Therefore, this study investigates the usage of Particle Swarm Optimization in Deep CNN models to improve accuracy. The focus of the study is on exploring different feature extractors for the age estimation task, utilizing pre-trained CNN models such as VGG16, VGG19, ResNet50, and Xception. The proposed approach utilizes PSO to optimize the hyperparameters of a custom output layer for age detection in regression. The PSO algorithm searches for the optimal combination of model hyperparameters that minimize the age estimation error. This study shows that fine-tuning a model can lead to improvements in its performance, with the VGG19 model achieving the best performance after fine-tuning. Additionally, the PSO process was able to find sets of hyperparameters that were on par or even better than the initial hyperparameters. The best result can be seen in VGG19 architecture with loss of 86.181, MAE of 6.693, and MAPE of 38.462. Out of the twelve experiments conducted, it was observed that the utilization of Particle Swarm Optimization (PSO) offered distinct advantages in terms of achieving better results for age estimation. However, it is important to note that the execution time for these experiments was considerably longer when employing PSO.

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Section: Resultsmentioning

confidence: 99%

Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization

Muliawan,

Angky,

Prasetyo

2023

E3S Web Conf.

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“…In the Eqs. ( 10) and (11), the disparities between all nearby pixel values and the central pixel value are likely to exceed h in the edge areas. The pixel value I SMF (x, y) is equal to I in this situation (x, y).…”

Section: Hybrid Selective Mean Filtering (Hsmf) Methodsmentioning

confidence: 99%

Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN

Nur-A-Alam,

Nasir,

Ahsan

et al. 2023

Sci Rep

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The COVID-19 disease caused by coronavirus is constantly changing due to the emergence of different variants and thousands of people are dying every day worldwide. Early detection of this new form of pulmonary disease can reduce the mortality rate. In this paper, an automated method based on machine learning (ML) and deep learning (DL) has been developed to detect COVID-19 using computed tomography (CT) scan images extracted from three publicly available datasets (A total of 11,407 images; 7397 COVID-19 images and 4010 normal images). An unsupervised clustering approach that is a modified region-based clustering technique for segmenting COVID-19 CT scan image has been proposed. Furthermore, contourlet transform and convolution neural network (CNN) have been employed to extract features individually from the segmented CT scan images and to fuse them in one feature vector. Binary differential evolution (BDE) approach has been employed as a feature optimization technique to obtain comprehensible features from the fused feature vector. Finally, a ML/DL-based ensemble classifier considering bagging technique has been employed to detect COVID-19 from the CT images. A fivefold and generalization cross-validation techniques have been used for the validation purpose. Classification experiments have also been conducted with several pre-trained models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier technique with fused feature has provided state-of-the-art performance with an accuracy of 99.98%.

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“…Chest X-rays and CT scans are indeed valuable tools in assessing the severity of lung involvement in COVID-19 patients [39,40]. These imaging techniques can reveal characteristic patterns of lung abnormalities associated with the virus, such as ground-glass opacities and consolidations.…”

Section: Imaging Techniques (Chest X-rays and Ct Scans)mentioning

confidence: 99%

Comprehensive Review of COVID-19: Symptoms, Diagnosis, and Management

2024

austinjautismrelatdisabil

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COVID-19, caused by the novel coronavirus SARS-CoV-2, can lead to a wide range of symptoms, varying from mild to severe. Common symptoms include fever, cough, and shortness of breath. Other symptoms may include fatigue, muscle or body aches, headache, loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, and diarrhea. Some individuals may also experience more severe symptoms such as difficulty breathing, chest pain or pressure, confusion, inability to stay awake, and bluish lips or face, which require immediate medical attention. It’s important to note that symptoms can appear 2-14 days after exposure to the virus, and some individuals may remain asymptomatic carriers. If you suspect you have COVID-19 or have been exposed to the virus, it’s crucial to seek medical advice and follow public health guidelines to prevent the spread of the virus. COVID-19 diagnosis involves identifying the presence of the SARS-CoV-2 virus through various methods such as RT-PCR testing, antigen testing, antibody testing, and imaging techniques such as chest X-rays and CT scans. Early and accurate diagnosis is crucial for effective management and control of the disease. Rapid and widespread testing, along with proper isolation and contact tracing, are essential for containing the spread of the virus. COVID-19 management involves a multifaceted approach, including vaccination, testing, contact tracing, and public health measures such as mask-wearing and social distancing. Treatment focuses on supportive care and, in some cases, antiviral medications. On-going research is crucial for developing effective strategies to control the spread of the virus and mitigate its impact on public health.

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Federated Learning Approach for Early Detection of Chest Lesion Caused by COVID-19 Infection Using Particle Swarm Optimization

Cited by 17 publications

References 47 publications

Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization

Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization

Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN

Comprehensive Review of COVID-19: Symptoms, Diagnosis, and Management

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