Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network

Çelik, Gaffari

doi:10.1016/j.asoc.2022.109906

Cited by 49 publications

(24 citation statements)

References 91 publications

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“…“Shortcut connections” or “skip connections” allow information to travel from layer to layer and help the network learn the difference between input and output. ResNet's residual block is multilayered, 47 mixing its output with the original input before passing on. In numbers,

\begin{equation}\ \ {X}_{out} = \ F\left( {{X}_{in} + \ {X}_{in}} \right)\end{equation}

…”

Section: Methodsmentioning

confidence: 99%

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things

Akram,

Rashid,

Jaffar

et al. 2023

Skin Research and Technology

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IntroductionParticularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques.MethodThis research uses a hybrid deep learning model that combines two cutting‐edge approaches: Mask Region‐based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset.ResultsThe experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state‐of‐the‐art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well.ConclusionIn conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis.

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\begin{equation}\ \ {X}_{out} = \ F\left( {{X}_{in} + \ {X}_{in}} \right)\end{equation}

…”

Section: Methodsmentioning

confidence: 99%

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things

Akram,

Rashid,

Jaffar

et al. 2023

Skin Research and Technology

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“…The important radiographic technique of CT scans provides high-resolution representations of internal tissues within the lung with highlights of changes, through which the extent of lung infection with pneumonia is well known [49]. In comparison, the RT-PCR analysis can include inaccuracies that require repeating the analysis process to confirm an infection, leading to more frequent decisions to apply the computerized tomography techniques to support infection diagnosis further [50].…”

Section: Ct Scan Datasetmentioning

confidence: 99%

Deep learning‐based COVID‐19 diagnosis using CT scans with laboratory and physiological parameters

Sameer

Mutlag

Gharghan

2023

IET Image Processing

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The global economy has been dramatically impacted by COVID‐19, which has spread to be a pandemic. COVID‐19 virus affects the respiratory system, causing difficulty breathing in the patient. It is crucial to identify and treat infections as soon as possible. Traditional diagnostic reverse transcription‐polymerase chain reaction (RT‐PCR) methods require more time to find the infection. A high infection rate, slow laboratory analysis, and delayed test results caused the widespread and uncontrolled spread of the disease. This study aims to diagnose the COVID‐19 epidemic by leveraging a modified convolutional neural network (CNN) to quickly and safely predict the disease's appearance from computed tomography (CT) scan images and a laboratory and physiological parameters dataset. A dataset representing 500 patients was used to train, test, and validate the CNN model with results in detecting COVID‐19 having an accuracy, sensitivity, specificity, and F1‐score of 99.33%, 99.09%, 99.52%, and 99.24%, respectively. These experimental results suggest that our strategy performs better than previously published approaches.

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“…9,10 Pneumonia or respiratory failure are common complications in COVID-19 cases and can be deadly. [11][12][13][14] The chance of developing a severe disease from COVID-19 can rise with age and certain preexisting medical conditions. Comorbidities like hypertension, obesity, diabetes, and smoking are very common in Mexico, which raises the risk of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

“…The severity of the disease is modulated by coexisting medical disorders 9,10 . Pneumonia or respiratory failure are common complications in COVID‐19 cases and can be deadly 11‐14 …”

Section: Introductionmentioning

confidence: 99%

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Hospitalized COVID‐19 patients with diabetes have an increased risk for pneumonia, intensive care unit requirement, intubation, and death: A cross‐sectional cohort study in Mexico in 2020

Huang

2023

Health Science Reports

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Background: Diabetes mellitus is a chronic health condition that has been linked with an increased risk of severe illness and mortality from COVID-19. In Mexico, the impact of diabetes on COVID-19 outcomes in hospitalized patients has not been fully quantified. Understanding the increased risk posed by diabetes in this patient population can help healthcare providers better allocate resources and improve patient outcomes. Objective: The objective of this study was to quantify the extent outcomes (pneumonia, intensive care unit [ICU] stay, intubation, and death) are worsened in diabetic patients with COVID-19. Methods: Between April 14, 2020 and December 20, 2020 (last accessed), data from the open-source COVID-19 database maintained by the Mexican Federal Government were examined. Utilizing hospitalized COVID-19 patients with complete outcome data, a retrospective cohort study (N = 402,388) was carried out. In relation to COVID-19, both univariate and multivariate logistic regression were used to investigate the effect of diabetes on specific outcomes. Results: The analysis included 402,388 adults (age >18) with confirmed hospitalized COVID-19 cases with mean age 46.16 (standard deviation = 15.55), 214,161 (53%) male. The outcomes delineated included pneumonia (N = 88,064; 22%), ICU

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Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network

Cited by 49 publications

References 91 publications

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things

Deep learning‐based COVID‐19 diagnosis using CT scans with laboratory and physiological parameters

Hospitalized COVID‐19 patients with diabetes have an increased risk for pneumonia, intensive care unit requirement, intubation, and death: A cross‐sectional cohort study in Mexico in 2020

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