Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Conference Object Citation - Scopus: 1Enhancing Quality Control in Plastic Injection Production: Deep Learning-Based Detection and Classification of Defects(IEEE, 2023) Mutlu, İsmail; Çakar, Tuna; Çakar, Tuna; Yıldız, Ahmet; Sayar, Alperen; Şimsek, Kamil; Tunalı, Mustafa Mert; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study investigates the applicability of diverse deep learning techniques in detecting and classifying defects within plastic injection manufacturing processes. The findings derived from the models yield several feasible solutions that hold potential practical implications. Notably, the implementation of the Xception model as a classification framework presents a potential domain for enhancing quality control procedures. The developed models, trained on the prepared data sets, provide compelling evidence for the potential utilization of artificial intelligence technologies in the manufacturing industry. Consequently, this study represents a noteworthy contribution to the limited yet auspicious academic research in the field.Conference Object Citation - Scopus: 10Steel Surface Defect Classification Via Deep Learning(IEEE, 2022) Yildiz, Ahmet; Çakar, Tuna; Tunal, Mustafa Mert; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityDeep learning and image processing methods have taken place in many parts of our lives, as well as in the quality control stages of production lines. The aim of this study is to train and use a deep learning model to improve quality management using limited data and computing power. To achieve that, deep learning for quality control models were trained by classifying six different steel surface defect images in the NEU-DET dataset. Xception, ResNetV2 152, VGG19 and InceptionV3 architectures were used to train the model. High accuracy was obtained with both Xception and ResNetV2 152. © 2022 IEEE.Article Citation - WoS: 1Citation - Scopus: 4Classification of Skin Lesion Images With Deep Learning Approaches(University of Latvia, 2022) Kulavuz, Bahadır; Çakar, Tuna; Bakırman, Tolga; Çakar, Tuna; Doğan, Metehan; Bayram, Bülent; Bayram, Buket; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversitySkin cancer is one of the most dangerous cancer types in the world. Like any other cancer type, early detection is the key factor for the patient's recovery. Integration of artificial intelligence with medical image processing can aid to decrease misdiagnosis. The purpose of the article is to show that deep learning-based image classification can aid doctors in the healthcare field for better diagnosis of skin lesions. VGG16 and ResNet50 architectures were chosen to examine the effect of CNN networks on the classification of skin cancer types. For the implementation of these networks, the ISIC 2019 Challenge has been chosen due to the richness of data. As a result of the experiments, confusion matrices were obtained and it was observed that ResNet50 architecture achieved 91.23% accuracy and VGG16 architecture 83.89% accuracy. The study shows that deep learning methods can be sufficiently exploited for skin lesion image classification. © 2022 Baltic Journal of Modern Computing. All rights reserved.