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Öğe Artificial Intelligence Based Social Distance Monitoring in Public Areas(Int Information & Engineering Technology Assoc, 2022) Albayrak, AbdulkadirCOVID-19 is an infectious disease caused by a newly discovered coronavirus called SARS-CoV-2. There are two ways of contamination risk, namely spreading through droplets or aerosol-type spreading into the air with people's speech in crowded environments. The best way to prevent the spread of COVID-19 in a crowd public area is to follow social distance rules. Violation of the social distance is a common situation in areas where people frequently visit such as hospitals, schools and shopping centers. In this study, an artificial intelligence -based social distance determination study was developed in order to detect social distance violations in crowded areas. Within the scope of the study, a new dataset was proposed to determine social distance between pedestrians. The YOLOv3 algorithm, which is very successful in object detection, was compared with the SSD-MobileNET, which is considered to be a light weighted model, and the traditionally handcrafted methods Haar-like cascade and HOG methods. Inability to obtain depth information, which is one of the biggest problems encountered in monocular cameras, has been tried to be eliminated by perspective transformation. In this way, the social distance violation detected in specific area is notified by the system to the relevant people with a warning.Öğe Classification of Analyzable Metaphase Images by Extreme Learning Machines(2021) Albayrak, AbdulkadirA chromosome is a DNA molecule that contains the genetic material of an organism. Possible defects in chromosomes can cause structural and functional disorders in living things. Identifying the metaphase stages of cells is a critical step to identify problems in chromosomes. In this proposed study, the discriminative features of possible metaphase images were extracted with Gray level co-occurrence matrix and classified with the Extreme Learning Machines classification method. When the results were evaluated, it was observed that the proposed method was as successful as the deep learning methods in the literature. Especially in recent years, when online learning has become important, the need for re- training of deep learning-based algorithms after each validation will increase the importance of the proposed method in this field. The rapid increase in unlabelled data from each patient every day affects the duration of training and creates time and resource constraints. Fast and accurate modelling of such data with alternative machine learning methods will contribute to the studies in this area.Öğe Classification of analyzable metaphase images using transfer learning and fine tuning(Springer, 2021) Albayrak, AbdulkadirChromosomes are bodies that contain human genetic information. Chromosomal disorders can cause structural and functional disorders in individuals. Detecting the metaphase stages of the cells accurately is a crucial step to detect possible defects in chromosomes. Thus, it is vital at this stage to identify the identical chromosome of each chromosome, to perform the pairing process, and to identify problems arising from this process. In this study, it was investigated whether the analyzable metaphase images can be analyzed by using the transfer learning and fine tuning approaches of deep learning models. The weights of VGG16 and InceptionV3 models trained with ImageNet data set were transferred to this problem and the classification process was carried out. True positive ratio values are 99%(+/- 0.9) and 99%(+/- 0.9) for VGG and Inception networks, respectively. The classification performances obtained depending on the changing training set ratios are presented comparatively in figures. F-measure, precision, and recall values obtained for the VGG and Inception networks were observed as 99%(+/- 1.0) and 99%(+/- 1.0), respectively. F-measure, precision, and recall values of VGG and Inceptionv3 networks are also presented with respect to the ratio of training size. The obtained results have compared with the state-of-the-art methods in the literature and supported with the tables and graphics. The training phase was also accelerated by using transfer learning and fine tuning methods. Transfer learning and fine tuning processes have almost similar performance as the models used in the literature and trained from scratch in metaphase Graphical Abstract The Flowchart of the proposed system for classifying metaphase candidates detection.Öğe Classification of Cervical Precursor Lesions via Local Histogram and Cell Morphometric Features(Ieee-Inst Electrical Electronics Engineers Inc, 2023) Calik, Nurullah; Albayrak, Abdulkadir; Akhan, Asl; Turkmen, Ilknur; Capar, Abdulkerim; Toreyin, Behcet Ugur; Bilgin, GokhanCervical squamous intra-epithelial lesions (SIL) are precursor cancer lesions and their diagnosis is important because patients have a chance to be cured before cancer develops. In the diagnosis of the disease, pathologists decide by considering the cell distribution from the basal to the upper membrane. The idea, inspired by the pathologists' point of view, is based on the fact that cell amounts differ in the basal, central, and upper regions of tissue according to the level of Cervical Intraepithelial Neoplasia (CIN). Therefore, histogram information can be used for tissue classification so that the model can be explainable. In this study, two different classification schemes are proposed to show that the local histogram is a useful feature for the classification of cervical tissues. The first classifier is Kullback Leibler divergence-based, and the second one is the classification of the histogram by combining the embedding feature vector from morphometric features. These algorithms have been tested on a public dataset.The method we propose in the study achieved an accuracy performance of 78.69% in a data set where morphology-based methods were 69.07% and Convolutional Neural Network (CNN) patch-based algorithms were 75.77%. The proposed statistical features are robust for tackling real-life problems as they operate independently of the lesions manifold.Öğe Derin öğrenme tabanlı YOLOv5 nesne tespiti yöntemi kullanılarak gaz tüpü tespiti(Institute of Electrical and Electronics Engineers Inc., 2022) Albayrak, Abdulkadir; Özerdem, Mehmet SiraçDetection and tracking of objects has critical importance in terms of speeding up the process and facilitating the work in many areas. Especially in the process of counting objects, which is difficult and time-consuming for experts. In this paper, a study was carried out to detect gas cylinders with different colors and shapes using the deep learning-based Yolov5 method. The process of counting cylinders in the stock area or in the filling facilities can be difficult for the specialist due to the different sizes, arrangement and large number of cylinders. Within the scope of the study, a data set containing different types of cylinders in gas filling facilities was created. When the obtained results are evaluated, it has been observed that the Yolov5 algorithm detects the gas cylinders with different color and shape properties with a high success rate of 96.16%. In addition to the detection success, it has been observed that the method is also successful in different objective detections such as precision, sensitivity and box intersection.Öğe Face Expression Recognition via transformer-based classification models(2024) Arslanoglu, M. Cihad; Acar, Hüseyin; Albayrak, AbdulkadirFacial Expression Recognition (FER) tasks have widely studied in the literature since it has many applications. Fast development of technology in deep learning computer vision algorithms, especially, transformer-based classification models, makes it hard to select most appropriate models. Using complex model may increase accuracy performance but decreasing infer- ence time which is a crucial in near real-time applications. On the other hand, small models may not give desired results. In this study, it is aimed to examine accuracy and data process time per- formance of 5 different relatively small transformer-based image classification algorithms for FER tasks. Used models are vanilla Vision Transformer (ViT), Pooling-based Vision Transformer (PiT), Shifted Windows Transformer (Swin), Data-efficient image Transformers (DeiT), and Cross-attention Vision Transformer (CrossViT) with considering their trainable parameter size and architectures. Each model has 20-30M trainable parameters which means relatively small. Moreover, each model has different architectures. As an illustration, CrossViT focuses on image using multi-scale patches and PiT model introduces convolution layers and pooling techniques to vanilla ViT model. Model performances are evaluated on CK+48 and KDEF datasets that are well- known and most used in the literature. It was observed that all models exhibit similar performance with literature results. PiT model that includes both Convolutional Neural Network (CNN) and Transformer layers achieved the best accuracy scores 0.9513 and 0.9090 for CK+48 and KDEF datasets, respectively. It shows CNN layers boost performance of Transformer based models and help to learn data more efficiently for CK+48 and KDEF datasets. Swin Transformer performs 0.9080 worst accuracy score for CK+48 dataset and 0.8434 nearly worst score for KDEF dataset. Swin Transformer and PiT exhibit worst and best image processing performance in terms of spent time, respectively. This makes PiT model suitable for real-time applications too. Moreover, PiT model require 25 and 83 second least training epoch to reach these performance for CK+48 and KDEF, respectively.Öğe Metaphase finding with deep convolutional neural networks(Elsevier Sci Ltd, 2019) Moazzen, Yaser; Capar, Abdulkerim; Albayrak, Abdulkadir; Calik, Nurullah; Toreyin, Behcet UgurBackground: Finding analyzable metaphase chromosome images is an essential step in karyotyping which is a common task for clinicians to diagnose cancers and genetic disorders precisely. This step is tedious and time-consuming. Hence developing automated fast and reliable methods to assist clinical technicians becomes indispensable. Previous approaches include methods with feature extraction followed by rule or quality based classifiers, component analysis, and neural networks. Methods: A two-stage automated metaphase-finding scheme, consisting of an image processing based metaphase detection stage, and a deep convolutional neural network based selection stage is proposed. The first stage detects metaphase images from 10x scan of specimen slides. The selection stage, on the other hand, selects the analyzable ones among them. Results: The proposed scheme has a 99.33% true positive rate and 0.34% of the false positive rate of metaphase finding. Conclusion: This study demonstrates an effective scheme for the automated finding of analyzable metaphase images with high True positive and low False positive rates. (C) 2019 Elsevier Ltd. All rights reserved.Öğe A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability(Springer Science and Business Media Deutschland GmbH, 2021) Albayrak, Abdulkadir; Akhan, Asli Ünlü; Çalık, Nurullah; Çapar, Abdulkerim; Bilgin, Gökhan; Töreyin, Behçet Uğur; Müezzinoğlu, Bahar; Türkmen, İlknur Çetinaslan; Durak Ata, LütfiyeThe cervical cancer developing from the precancerous lesions caused by the human papillomavirus (HPV) has been one of the preventable cancers with the help of periodic screening. Cervical intraepithelial neoplasia (CIN) and squamous intraepithelial lesion (SIL) are two types of grading conventions widely accepted by pathologists. On the other hand, inter-observer variability is an important issue for final diagnosis. In this paper, a whole-slide image grading benchmark for cervical cancer precursor lesions is created and the “Uterine Cervical Cancer Database” introduced in this article is the first publicly available cervical tissue microscopy image dataset. In addition, a morphological feature representing the angle between the basal membrane (BM) and the major axis of each nucleus in the tissue is proposed. The presence of papillae of the cervical epithelium and overlapping cell problems are also discussed. Besides that, the inter-observer variability is also evaluated by thorough comparisons among decisions of pathologists, as well as the final diagnosis.
