Smartphone-based Multi-parametric Glucose Prediction using Recurrent Neural Networks

2022

EJOSAT

Cerebrovascular disease (CVD) causes paralysis and even mortality in humans due to blockage or bleeding of brain vessels. The early diagnosis of the CVD type by the specialist can avoid these casualties with a correct course of treatment. However, it is not always possible to recruit enough specialists in hospitals or emergency services. Therefore, in this study, an artificial intelligence (AI)-based clinical decision support system for CVD detection from brain computed tomography (CT) images is proposed to improve the diagnostic results and relieve the burden of specialists. The deep learning model, a subset of AI, was implemented through a two-step process in which CVD is first detected and then classified as ischemic or hemorrhagic. Moreover, the developed system is integrated into our custom-designed desktop application that offers a user-friendly interface for CVD diagnosis. Experimental results prove that our system has great potential to improve early diagnosis and treatment for specialists, which contributes to the recovery rate of patients.

Section: Specialists Use Medical Imaging Techniques Such As Magneticmentioning

confidence: 99%

Beyin Bilgisayarlı Tomografi Görüntülerinde Yapay Zeka Tabanlı Beyin Damar Hastalıkları Tespiti

KARATAŞ

Doğan

2022

EJOSAT

“…In addition, deep learning‐based methods show promising results in image segmentation, 19 which leads to being employed in many medical imaging applications 20 . Deep learning methods are based on neural network architectures, including convolutional neural networks (CNNs), 21 recurrent neural networks (RNN), 22 autoencoders, 23 transformers, 24 and deep belief nets 25 . Among these architectures, CNNs show outstanding performance in processing digital images 26 .…”

Section: Introductionmentioning

confidence: 99%

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

AĞRALI

Int J Imaging Syst Tech

Onan

et al. 2023

The conventional approach for identifying ground glass opacities (GGO) in medical imaging is to use a convolutional neural network (CNN), a subset of artificial intelligence, which provides promising performance in COVID-19 detection. However, CNN is still limited in capturing structured relationships of GGO as the texture and shape of the GGO can be confused with other structures in the image. In this paper, a novel framework called DeepChestNet is proposed that leverages structured relationships by jointly performing segmentation and classification on the lung, pulmonary lobe, and GGO, leading to enhanced detection of COVID-19 with findings. The performance of Deep-ChestNet in terms of dice similarity coefficient is 99.35%, 99.73%, and 97.89% for the lung, pulmonary lobe, and GGO segmentation, respectively. The experimental investigations on DeepChestNet-Lung, DeepChestNet-Lobe and Deep-ChestNet-COVID datasets, and comparison with several state-of-the-art approaches reveal the great potential of DeepChestNet for diagnosis of COVID-19 disease.

“…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). Among these architectures, CNN offers remarkable performance on ischemic stroke disease segmentation.…”

Section: Introductionmentioning

confidence: 99%

Beyin Bilgisayarlı Tomografi Görüntülerinde Derin Öğrenme Tabanlı İskemik İnme Hastalığı Segmentasyonu

UÇKUN

AĞRALI

2023

EJOSAT

Self 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.