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Öğe Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features(Springer, 2022) Sığırcı, İ. Onur; Albayrak, Abdülkadir; Bilgin, GökhanOne of the most important processes in the diagnosis of breast cancer, which is the leading mortality rate in women, is the detection of the mitosis stage at the cellular level. In literature, many studies have been proposed on the computer-aided diagnosis (CAD) system for detecting mitotic cells in breast cancer histopathological images. In this study, comparative evaluation of conventional and deep learning based feature extraction methods for automatic detection of mitosis in histopathological images are focused. While various handcrafted features are extracted with textural/spatial, statistical and shape-based methods in conventional approach, the convolutional neural network structure proposed on the deep learning approach aims to create an architecture that extracts the features of small cellular structures such as mitotic cells. Mitosis detection/counting is an important process that helps us assess how aggressive or malignant the cancer's spread is. In the proposed study, approximately 180,000 non-mitotic and 748 mitotic cells are extracted for the evaluations. It is obvious that the classification stage cannot be performed properly due to the imbalanced numbers of mitotic and non-mitotic cells extracted from histopathological images. Hence, the random under-sampling boosting (RUSBoost) method is exploited to overcome this problem. The proposed framework is tested on mitosis detection in breast cancer histopathological images dataset provided from the International Conference on Pattern Recognition (ICPR) 2014 contest. In the results obtained with the deep learning approach, 79.42% recall, 96.78% precision and 86.97% F-measure values are achieved more successfully than handcrafted methods. A client/server-based framework has also been developed as a secondary decision support system for use by pathologists in hospitals. Thus, it is aimed that pathologists will be able to detect mitotic cells in various histopathological images more easily through necessary interfaces.Öğe Face Expression Recognition via transformer-based classification models(MUSA YILMAZ, 2024) Arslanoğlu, Muhammed Cihad; Acar, Hüseyin; Albayrak, AbdülkadirFacial 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 inference time which is a crucial in near real-time applications. On the other hand, small models may not give desired results. In this study, we aimed to examine performance of 5 different relatively small transformer-based image classification algorithms for FER tasks. We used vanilla ViT, PiT, Swin, DeiT, and 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. We obtained all results for widely used FER datasets: CK+ and KDEF. We observed that, PiT model achieves the best accuracy scores 0.9513 and 0.9090 for CK+ and KDEF datasets, respectivelyÖğe Öz dikkat mekanizması tabanlı görü dönüştürücü kullanılarak sıtma parazit tespiti(Dicle Üniversitesi Mühendislik Fakültesi, 2022) Tuncel, İbrahim; Albayrak, Abdülkadir; Akın, MehmetSıtma, tedavisiz olgularda ölümle sonuçlanabilen ve ciddi ateşli hastalığa yol açan bir enfeksiyon hastalığıdır. Bu yüzden bu hastalığın erken tanı ve tedavisi oldukça kritik öneme sahiptir. Gelişmiş teknolojilerle birlikte sıtma hastalığının teşhisine yönelik birçok klinik yöntem ve test kullanılmaktadır. Bu çalışmada Sıtma hastalığının teşhis edilmesi amacıyla son yıllarda doğal dil işleme alanında oldukça yüksek performans gösteren transformer yöntemlerden esinlenilerek önerilen Vision Transformer (ViT) yöntemi kullanılmaktadır. Elde edilen sonuçlar değerlendirildiğinde ViT yönteminin %97.22 gibi yüksek bir sınıflandırma performansı elde ettiği gözlemlenmiştir. Vit yöntemi ile elde edilen sonuçlar, literatürde uygulanan geleneksel ve derin öğrenme yöntemleri karşılaştırılmış ve bu sonuçlar karşılaştırmalı olarak tabloda sunulmuştur. Uygulanan ViT modelinin sıtma hastalığı tespitinde başarılı sonuç verdiği gözlemlenmiştir.Öğe Vision Transformer Based Photo Capturing System(MUSA YILMAZ, 2023) Albayrak, AbdülkadirPortrait photo is one of the most crucial documents that many people need for official transactions in many public and private organizations. Despite the developing technologies and high resolution imaging devices, people need such photographer offices to fulfil their needs to take photos. In this study, a Photo Capturing System has been developed to provide infrastructure for web and mobile applications. After the system detects the person's face, facial orientation and facial expression, it automatically takes a photo and sends it to a graphical user interface developed for this purpose. Then, with the help of the user interface of the photo taken by the system, it is automatically printed out. The proposed study is a unique study that uses imaging technologies, deep learning and vision transformer algorithms, which are very popular image processing techniques in several years. Within the scope of the study, face detection and facial expression recognition are performed with a success rate of close to 100\% and 95.52\%, respectively. In the study, the performances of Vision Transformer algorithm is also compared with the state of art algorithms in facial expression recognition.
