RETRACTED ARTICLE: A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection

Kaya, Yasin; Gürsoy, Ercan

doi:10.1007/s00500-022-07798-y

Cited by 56 publications

(22 citation statements)

References 73 publications

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“…They used 3616 COVID-19 and 1576 normal (healthy) and 4265 pneumonia X-ray images. Their models achieved an average accuracy rate of 97.61% for 3-class cases with fivefold cross-validation [ 2 ].…”

Section: Summary Of the Research Methodsmentioning

confidence: 99%

“…Coronavirus 2 (SARS-CoV-2) is an infectious virus that causes severe acute respiratory syndrome and is recognized as COVID-19 disease [ 1 , 2 ]. Coronaviruses are large, positively spliced, single-stranded RNA viruses [ 3 ] that infect humans and a variety of other living organisms [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Identification of these individuals may prevent the further spread of the pandemic, as COVID-19 infections manifest as pulmonary infections. Therefore, computed tomography scans (CT) and chest X-rays can be used to detect COVID-19 disease [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

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An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works

Gürsoy

Kaya

2023

Multimedia Systems

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The World Health Organization (WHO) declared a pandemic in response to the coronavirus COVID-19 in 2020, which resulted in numerous deaths worldwide. Although the disease appears to have lost its impact, millions of people have been affected by this virus, and new infections still occur. Identifying COVID-19 requires a reverse transcription-polymerase chain reaction test (RT-PCR) or analysis of medical data. Due to the high cost and time required to scan and analyze medical data, researchers are focusing on using automated computer-aided methods. This review examines the applications of deep learning (DL) and machine learning (ML) in detecting COVID-19 using medical data such as CT scans, X-rays, cough sounds, MRIs, ultrasound, and clinical markers. First, the data preprocessing, the features used, and the current COVID-19 detection methods are divided into two subsections, and the studies are discussed. Second, the reported publicly available datasets, their characteristics, and the potential comparison materials mentioned in the literature are presented. Third, a comprehensive comparison is made by contrasting the similar and different aspects of the studies. Finally, the results, gaps, and limitations are summarized to stimulate the improvement of COVID-19 detection methods, and the study concludes by listing some future research directions for COVID-19 classification.

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Section: Summary Of the Research Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works

Gürsoy

Kaya

2023

Multimedia Systems

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“…These systems were developed using the concept of transfer learning. Hence, transfer learning is a technique used for training the model where the knowledge of the previously trained model is transferred and used for a different but related purpose [43,44].…”

Section: -1-relevant Studiesmentioning

confidence: 99%

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Francis¹,

Anbananthen

Deisy³

et al. 2023

Emerg Sci J

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Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PDF

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“…A timeefficient generalized model with residual separable convolution block improves the performance of the basic MobileNet model in Tangudu, Kakarla, and Venkateswarlu (2022). In addition, Kaya and Gürsoy Kaya and Gürsoy (2023) classified CXR images into COVID-19, Normal, and Pneumonia using transfer learning on the pre-trained network MobileNetV2. They Developed an enhanced finetuning approach that minimized the data loss and increased the number of transfer features.…”

Section: Related Workmentioning

confidence: 99%

MutliFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification

Agarwal

Arya

Meena

2023

Preprint

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Convolutional neural networks have been utilised for medical disease detection. However, most of the work is completed on the feature maps of the final convolution layer and the benefits of additional layers are not considered. The extraction of effective features from a limited medical image dataset is key for improving disease classification. In this paper, we propose a novel deep learning-based multilayer multimodal fusion model that focuses on extracting the features of different layers of the model and their fusion. Our disease detection model considered discriminatory information from each layer. Furthermore, to fuse different-sized feature maps of layers, we propose a novel feature map transform module known as the fusion of differentsize feature maps (FDSFM). The proposed model has shown a significantly higher accuracy of 97.21% and 99.60% for both three-class classification and two-class classification, respectively. The proposed model can be extended to any disease classifications from chest X-Ray images.

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RETRACTED ARTICLE: A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection

Cited by 56 publications

References 73 publications

An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works

An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works

Smart Farm-Care using a Deep Learning Model on Mobile Phones

MutliFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification

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