<scp>DeepChestNet</scp>: Artificial intelligence approach for <scp>COVID</scp>‐19 detection on computed tomography images

AĞRALI, Mahmut; Kılıç, Volkan; Onan, Aytuğ; Koç, Esra Meltem; Koç, Ali Murat; Büyüktoka, Raşit Eren; Acar, Türker; Adıbelli, Zehra Hilal

doi:10.1002/ima.22876

Cited by 6 publications

(2 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computed Tomography (CT) is an imaging strategy used to examine the internal organs of the body. It is otherwise called modernized tomography or electronic axial tomography (CAT).CT machines take constant pictures in a helical way as opposed to taking a progression of pictures of individual cuts of the body [8].…”

Section: Methodsmentioning

confidence: 99%

Detection of Covid-19 Using AI Application

Ravikumar

Ishaque²,

Panigrahi³

et al. 2023

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

INTRODUCTION: In December of 2019, the infection which caused the pandemic started in the Hubei territory of Wuhan, China. They were identified as SARS-CoV-2, a highly infectious, easily transmissible virus that has caused an increasing number of deaths worldwide. Covid can be perceived with a testing strategy known as RT-PCR. As of now, this technique is broadly utilized for identifying the infection. OBJECTIVES: The imaging modalities are utilized for various degrees of seriousness from asymptomatic to basic cases. Side effects of an individual contaminated with COVID-19 incorporate gentle hack, fever, chest torment, weakness, and so forth An individual with an extremefundamental ailment requires basic consideration. Imaging has assumed a larger part during the flare-up, with CT being a better option than invert transcriptase-polymerase chain response testing. METHODS: With artificial intelligence and robotics, a variety of devices and solutions have been introduced to improve contactless service forhumans. The presentation of AI technology may be a distinct advantage for the contactless treatment of patients. Information technology and AI could solve the testing and tracking system without any human interaction. RESULTS: CT imaging methods permit radiologists and doctors to distinguish inner structures and see their shape, size, thickness, and surface,which could help in the early discovery of asymptomatic cases. CONCLUSION: This detailed information data can be utilized to decide whether there's a clinical issue, provide the extent and accurate area of the matter, and uncover other significant details which will assist the doctor with deciding the best treatment.

show abstract

Section: Methodsmentioning

confidence: 99%

Detection of Covid-19 Using AI Application

Ravikumar

Ishaque²,

Panigrahi³

et al. 2023

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

show abstract

“…Therefore, deep learning approaches have gained popularity in segmenting ischemic stroke disease regions on CT images. For feature extraction, deep learning uses network architectures, such as convolutional neural networks (CNNs) (Ağralı et al;Akosman, Öktem, Moral, & Kılıç, 2021;Çaylı, Kılıç, Onan, & Wang, 2022;Keskin, Moral, Kılıç, & Onan, 2021;B. Kilic, Dogan, Kilic, & Kahyaoglu, 2022;Sayraci, Agrali, & Kilic, 2023;Şen et al, 2022;Yüzer, Doğan, Kılıç, & Şen, 2022), reinforcement learning (Agrali, Soydemir, Gökçen, & Sahin, 2021), and recurrent neural networks (RNNs) (Aydın, Çaylı, Kılıç, & Onan, 2022;Fetiler, Caylı, Moral, Kılıc, & Onan, 2021;Gölcez, Kiliç, & Şen, 2019;Keskin, Çaylı, Moral, Kılıc, & Onan, 2021;Kılıc, 2021;Volkan Kılıç;Kökten & Kılıç, 2021;Mercan, Doğan, & Kılıç, 2020;Mercan & Kılıç, 2021;Palaz, Doğan, & Kılıç, 2021).…”

Section: Introductionmentioning

confidence: 99%

Beyin Bilgisayarlı Tomografi Görüntülerinde Derin Öğrenme Tabanlı İskemik İnme Hastalığı Segmentasyonu

UÇKUN

AĞRALI

Kılıç

2023

EJOSAT

Self Cite

View full text Add to dashboard Cite

Stroke is brain cell death because of either lack of blood flow (ischemic) or bleeding (hemorrhagic) that prevents the brain from functioning properly in both conditions. Ischemic stroke is a common type of stroke caused by a blockage in the cerebrovascular system that prevents blood from flowing to brain regions and directly blocks blood vessels. Computed tomography (CT) scanning is frequently used in the evaluation of stroke, and rapid and accurate diagnosis of ischemic stroke with CT images is critical for determining the appropriate treatment. The manual diagnosis of ischemic stroke can be error-prone due to several factors, such as the busy schedules of specialists and the large number of patients admitted to healthcare facilities. Therefore, in this paper, a deep learning-based interface was developed to automatically diagnose the ischemic stroke through segmentation on CT images leading to a reduction on the diagnosis time and workload of specialists. Convolutional Neural Networks (CNNs) allow automatic feature extraction in ischemic stroke segmentation, utilized to mark the disease regions from CT images. CNN-based architectures, such as U-Net, U-Net VGG16, U-Net VGG19, Attention U-Net, and ResU-Net, were used to benchmark the ischemic stroke disease segmentation. To further improve the segmentation performance, ResU-Net was modified, adding a dilation convolution layer after the last layer of the architecture. In addition, data augmentation was performed to increase the number of images in the dataset, including the ground truths for the ischemic stroke disease region. Based on the experimental results, our modified ResU-Net with a dilation convolution provides the highest performance for ischemic stroke segmentation in dice similarity coefficient (DSC) and intersection over union (IoU) with 98.45 % and 96.95 %, respectively. The experimental results show that our modified ResU-Net outperforms the state-of-the-art approaches for ischemic stroke disease segmentation. Moreover, the modified architecture has been deployed into a new desktop application called BrainSeg, which can support specialists during the diagnosis of the disease by segmenting ischemic stroke.

show abstract

Smartphone‐Embedded Artificial Intelligence‐Based Regression for Colorimetric Quantification of Multiple Analytes with a Microfluidic Paper‐Based Analytical Device in Synthetic Tears

Baştürk,

Yüzer,

şen

et al. 2024

Advanced Intelligent Systems

View full text Add to dashboard Cite

Artificial intelligence (AI) and smartphones have attracted significant interest in microfluidic paper‐based colorimetric sensing due to their convenience and robustness. Recently, AI‐based classification of colorimetric assays has been increasingly reported. However, quantitative evaluation remains a challenge, as classification aims to categorize the color change into discrete class labels rather than a quantity. Therefore, in this study, an AI‐based regression model with enhanced accuracy is developed and integrated into a microfluidic paper‐based analytical device for simultaneous colorimetric measurements of glucose, cholesterol, and pH. The model is also embedded into a smartphone via a custom‐designed Android application named ChemiCheck to complete on‐site colorimetric quantification without internet access in under 1 s. The results demonstrate that the integrated system is able to sensitively detect both glucose (limit of detection [LOD]: 131 ) and cholesterol (LOD: 217 ), concluding the entire analysis within minutes while maintaining a maximum root mean square error of 0.386. Overall, the integrated platform holds great promise for point‐of‐care testing and offers numerous advantages, including easy‐to‐use operation, rapid response, low‐cost, high selectivity, and consistent repeatability, particularly in nonlaboratory and resource‐limited environments.

show abstract

DeepChestNet: Artificial intelligence approach for COVID‐19 detection on computed tomography images

Cited by 6 publications

References 63 publications

Detection of Covid-19 Using AI Application

Detection of Covid-19 Using AI Application

Beyin Bilgisayarlı Tomografi Görüntülerinde Derin Öğrenme Tabanlı İskemik İnme Hastalığı Segmentasyonu

Smartphone‐Embedded Artificial Intelligence‐Based Regression for Colorimetric Quantification of Multiple Analytes with a Microfluidic Paper‐Based Analytical Device in Synthetic Tears

Contact Info

Product

Resources

About