PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collections

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/5

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Citation - WoS: 11
Citation - Scopus: 19
Artificial Intelligence in Healthcare Competition (teknofest-2021): Stroke Data Set
(AVES, 2022) Koç, U.; Sezer, E.A.; Özkaya, Y.A.; Yarbay, Y.; Taydaş, O.; Ayyıldız, V.A.; Bahadır, Murat
Objective: The artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Türkiye, in September 2021. In this article, the data set preparation and competition processes were explained in detail; the anonymized and annotated data set is also provided via official website for further research. Materials and Methods: Data set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Türkiye, Ministry of Health using various codes and filtering criteria. The data set was anonymized. The data set was prepared, pooled, curated, and annotated by 7 radiologists. The training data set was shared with the teams via a dedicated file transfer protocol server, which could be accessed using private usernames and passwords given to the teams under a non-disclosure agreement signed by the representative of each team. Results: The competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to 1 of 3 possible categories namely, hemorrhage, ischemic, or non-stroke. Teams were asked to classify each image as either stroke present or absent. In the second stage of the competition, qualifying 36 teams were given 97 digital imaging and communications in medicine images that contained hemorrhage, ischemia, or both lesions. Among the employed methods, Unet and DeepLabv3 were the most frequently observed ones. Conclusion: Artificial intelligence competitions in healthcare offer good opportunities to collect data reflect-ing various cases and problems. Especially, annotated data set by domain experts is more valuable. © 2022, AVES. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 5
Covid-19 Isolation Control Proposal Via Uav and Ugv for Crowded Indoor Environments: Assistive Robots in the Shopping Malls
(Frontiers Media Sa, 2022) Aslan, Muhammet Fatih; Hasikin, Khairunnisa; Yusefi, Abdullah; Durdu, Akif; Sabancı, Kadir; Azizan, Muhammad Mokhzaini
Artificial intelligence researchers conducted different studies to reduce the spread of COVID-19. Unlike other studies, this paper isn't for early infection diagnosis, but for preventing the transmission of COVID-19 in social environments. Among the studies on this is regarding social distancing, as this method is proven to prevent COVID-19 to be transmitted from one to another. In the study, Robot Operating System (ROS) simulates a shopping mall using Gazebo, and customers are monitored by Turtlebot and Unmanned Aerial Vehicle (UAV, DJI Tello). Through frames analysis captured by Turtlebot, a particular person is identified and followed at the shopping mall. Turtlebot is a wheeled robot that follows people without contact and is used as a shopping cart. Therefore, a customer doesn't touch the shopping cart that someone else comes into contact with, and also makes his/her shopping easier. The UAV detects people from above and determines the distance between people. In this way, a warning system can be created by detecting places where social distance is neglected. Histogram of Oriented-Gradients (HOG)-Support Vector Machine (SVM) is applied by Turtlebot to detect humans, and Kalman-Filter is used for human tracking. SegNet is performed for semantically detecting people and measuring distance via UAV. This paper proposes a new robotic study to prevent the infection and proved that this system is feasible.
Citation - Scopus: 4
Deep Learning-Based Brain Hemorrhage Detection in Ct Reports
(IOS Press BV, 2022) Bayrak, Gıyaseddin; Toprak, M. Şakir; Ganiz, Murat Can; Kodaz, Halife; Koç, Ural
Radiology reports can potentially be used to detect critical cases that need immediate attention from physicians. We focus on detecting Brain Hemorrhage from Computed Tomography (CT) reports. We train a deep learning classifier and observe the effect of using different pre-trained word representations along with domain-specific fine-tuning. We have several contributions. Firstly, we report the results of a large-scale classification model for brain hemorrhage detection from Turkish radiology reports. Second, we show the effect of fine-tuning pre-trained language models using domain-specific data on the performance. We conclude that deep learning models can be used for detecting brain Hemorrhage with reasonable accuracy and fine-tuning language models using domain-specific data to improve classification performance. © 2022 European Federation for Medical Informatics (EFMI) and IOS Press.
Citation - WoS: 10
Citation - Scopus: 15
Multi-Class Brain Normality and Abnormality Diagnosis Using Modified Faster R-Cnn
(ELSEVIER IRELAND LTD, 2021) Uyar, Kübra; Taşdemir, Şakir; Ülker, Erkan; Öztürk, Mehmet; Kasap, Hüseyin
Background and Objective: The detection and analysis of brain disorders through medical imaging techniques are extremely important to get treatment on time and sustain a healthy lifestyle. Disorders cause permanent brain damage and alleviate the lifespan. Moreover, the classification of large volumes of medical image data manually by medicine experts is tiring, time-consuming, and prone to errors. This study aims to diagnose brain normality and abnormalities using a novel ResNet50 modified Faster Regions with Convolutional Neural Network(R-CNN) model. The classification task is performed into multiple classes which are hemorrhage, hydrocephalus, and normal. The proposed model both determines the borders of the normal/abnormal parts and classifies them with the highest accuracy. Methods: To provide a comprehensive performance analysis in the classification problem, Machine Learning(ML) and Deep Learning(DL) techniques were discussed. Artificial Neural Network(ANN), AdaBoost(AB), Decision Tree(DT), Logistic Regression(LR), Naive Bayes(NB), Random Forest(RF), and Support Vector Machine(SVM) were used as ML models. Besides, various Convolutional Neural Network(CNN) models and proposed ResNet50 modified Faster R-CNN model were used as DL models. Methods were validated using a novel brain dataset that contains both normal and abnormal images. Results: Based on results, LR obtained the highest result among ML methods and DenseNet201 obtained the highest results among CNN models with the accuracy of 84.80% and 85.68% for the classification task, respectively. Besides, the accuracy obtained by the proposed model is 99.75%. Conclusions: Experimental results demonstrate that the proposed model has yielded better performance for detection and classification tasks. This artificial intelligence(AI) framework can be utilized as a computer-aided medical decision support system for medical experts.
Citation - WoS: 31
Citation - Scopus: 34
A New Method for Automatic Counting of Ovarian Follicles on Whole Slide Histological Images Based on Convolutional Neural Network
(PERGAMON-ELSEVIER SCIENCE LTD, 2019) İnik, Özkan; Ceyhan, Ayşe; Balcıoğlu, Esra; Ülker, Erkan
The ovary is a complex endocrine organ that shows significant structural and functional changes in the female reproductive system over recurrent cycles. There are different types of follicles in the ovarian tissue. The reproductive potential of each individual depends on the numbers of these follicles. However, genetic mutations, toxins, and some specific drugs have an effect on follicles. To determine these effects, it is of great importance to count the follicles. The number of follicles in the ovary is usually counted manually by experts, which is a tedious, time-consuming and intense process. In some cases, the experts count the follicles in a subjective way due to their knowledge. In this study, for the first time, a method has been proposed for automatically counting the follicles of ovarian tissue. Our method primarily involves filter-based segmentation applied to whole slide histological images, based on a convolutional neural network (CNN). A new method is also proposed to eliminate the noise that occurs after the segmentation process and to determine the boundaries of the follicles. Finally, the follicles whose boundaries are determined are classified. To evaluate its performance, the results of the proposed method were compared with those obtained by two different experts and the results of the Faster R-CNN model. The number of follicles obtained by the proposed method was very close to the number of follicles counted by the experts. It was also found that the proposed method was much more successful than the Faster R-CNN model.

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