Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture

Talukder, Md. Alamin; Layek, Md. Abu; Kazi, Mohsin; Uddin, Md. Ashraf; Aryal, Sunil

doi:10.1016/j.compbiomed.2023.107789

Cited by 16 publications

(3 citation statements)

References 38 publications

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“…EfficientNet [118] Modified the last layer of EfficientNet by adding global average pooling, batch normalization, dense layers, and dropout layers.…”

Section: Model Name Brief Descriptions Aspects To Addressmentioning

confidence: 99%

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Sarkar,

Lee,

Sahoo

2024

Electronics

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Ambient Intelligence (AMI) represents a significant advancement in information technology that is perceptive, adaptable, and finely attuned to human needs. It holds immense promise across diverse domains, with particular relevance to healthcare. The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) to create an AMI environment in medical contexts further enriches this concept within healthcare. This survey provides invaluable insights for both researchers and practitioners in the healthcare sector by reviewing the incorporation of AMI techniques in the IoMT. This analysis encompasses essential infrastructure, including smart environments and spectrum for both wearable and non-wearable medical devices to realize the AMI vision in healthcare settings. Furthermore, this survey provides a comprehensive overview of cutting-edge AI methodologies employed in crafting IoMT systems tailored for healthcare applications and sheds light on existing research issues, with the aim of guiding and inspiring further advancements in this dynamic field.

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“…EfficientNet [118] Modified the last layer of EfficientNet by adding global average pooling, batch normalization, dense layers, and dropout layers.…”

Section: Model Name Brief Descriptions Aspects To Addressmentioning

confidence: 99%

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Sarkar,

Lee,

Sahoo

2024

Electronics

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“…Talukder et al [7] presented a deep transfer learning (DTL) approach to detecting COVID-19 using X-ray images, working as follows: First, they used two datasets, where the first had 2000 COVID-19 X-ray images pertaining to COVID-19 and normal cases, and the second had 4352 chest X-ray images pertaining to these four classes: COVID-19, normal, lung opacity, and viral pneumonia. They divided the two datasets and utilized an 80:10 training-to-validation split ratio, while the remaining was used for testing.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through the Deep Transfer Learning Approach

Turki,

Al Habib,

Taguchi

2024

Mathematics

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Transmission electron microscopy imaging provides a unique opportunity to inspect the detailed structure of infected lung cells with SARS-CoV-2. Unlike previous studies, this novel study aims to investigate COVID-19 classification at the lung cellular level in response to SARS-CoV-2. Particularly, differentiating between healthy and infected human alveolar type II (hAT2) cells with SARS-CoV-2. Hence, we explore the feasibility of deep transfer learning (DTL) and introduce a highly accurate approach that works as follows: First, we downloaded and processed 286 images pertaining to healthy and infected hAT2 cells obtained from the electron microscopy public image archive. Second, we provided processed images to two DTL computations to induce ten DTL models. The first DTL computation employs five pre-trained models (including DenseNet201 and ResNet152V2) trained on more than one million images from the ImageNet database to extract features from hAT2 images. Then, it flattens and provides the output feature vectors to a trained, densely connected classifier with the Adam optimizer. The second DTL computation works in a similar manner, with a minor difference in that we freeze the first layers for feature extraction in pre-trained models while unfreezing and jointly training the next layers. The results using five-fold cross-validation demonstrated that TFeDenseNet201 is 12.37× faster and superior, yielding the highest average ACC of 0.993 (F1 of 0.992 and MCC of 0.986) with statistical significance ( from a t-test) compared to an average ACC of 0.937 (F1 of 0.938 and MCC of 0.877) for the counterpart (TFtDenseNet201), showing no significance results ( from a t-test).

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“…Lung squamous cell carcinoma (SCC), which has an estimated frequency of 3% to 18%, is comparatively uncommon compared to lung adenocarcinoma (1–10) [ 7 ]. Lung cancer, which comprises both small and non‐small cells, is the leading cause of cancer‐related death globally [ 8 , 9 , 10 , 11 ]. The world's highest incidence and fatality rates are associated with the most prevalent kinds of cancer [ 12 ].…”

mentioning

confidence: 99%

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR‐mutated lung adenocarcinoma

Bhattacharjya,

Islam,

Uddin

et al. 2024

FEBS Open Bio

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Hypopharyngeal cancer gene regulatory networks and therapeutic molecules are a disease that is associated with EGFR‐mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

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Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture

Cited by 16 publications

References 38 publications

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence

Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through the Deep Transfer Learning Approach

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR‐mutated lung adenocarcinoma

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