Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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    Computer-Aided Colorectal Cancer Diagnosis: Ai-Driven Image Segmentation And Classification
    (Başkent Üniversitesi Mühendislik Fakültesi, 2024-05-31) Erdas, Cagatay Berke
    Colorectal cancer is an enormous health concern since it is among the most lethal types of malignancy. The manual examination has its limitations, including subjectivity and data overload. To overcome these challenges, computer -aided diagnostic systems focusing on image segmentation and abnormality classi fi cation have been developed. This study presents a two -stage approach for the automatic detection of fi ve types of colorectal abnormalities in addition to a control group: polyp, low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia, serrated adenoma, adenocarcinoma. In the fi rst stage, UNet3+ was used for image segmentation to locate the anomalies, while in the second stage, the Cross -Attention Multi -Scale Vision Transformer deep learning model was used to predict the type of anomaly after highlighting the anomaly on the raw images. In anomaly segmentation, UNet3+ achieved values of 0.9872, 0.9422, 0.9832, and 0.9560 for Dice Coef fi cient, Jaccard Index, Sensitivity, Speci fi city respectively. In anomaly detection, the Cross -Attention Multi -Scale Vision Transformer model attained a classi fi cation performance of 0.9340, 0.9037, 0.9446, 0.8723, 0.9102, 0.9849 for accuracy, F1 score, precision, recall, Matthews correlation coef fi cient, and speci fi city, respectively. The proposed approach proves its capacity to alleviate the overwhelm of pathologists and enhance the accuracy of colorectal cancer diagnosis by achieving high performance in both the identi fi cation of anomalies and the segmentation of regions.
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    Categorization Of Alzheimer's Disease Stages Using Deep Learning Approaches With Mcnemar's Test
    (Başkent Üniversitesi Mühendislik Fakültesi, 2024-03-13) Sener, Begum; Acici, Koray; Sumer, Emre
    Early diagnosis is crucial in Alzheimer's disease both clinically and for preventing the rapid progression of the disease. Early diagnosis with awareness studies of the disease is of great importance in terms of controlling the disease at an early stage. Additionally, early detection can reduce treatment costs associated with the disease. A study has been carried out on this subject to have the great importance of detecting Alzheimer's disease at a mild stage and being able to grade the disease correctly. This study's dataset consisting of MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) was split into training and testing sets, and deep learning -based approaches were used to obtain results. The dataset consists of three classes: Alzheimer's disease (AD), Cognitive Normal (CN), and Mild Cognitive Impairment (MCI). The achieved results showed an accuracy of 98.94% for CN vs AD in the one vs one (1 vs 1) classification with the EfficientNetB0 model and 99.58% for AD vs CNMCI in the one vs All (1 vs All) classification with AlexNet model. In addition, in the study, an accuracy of 98.42% was obtained with the EfficientNet121 model in MCI vs CN classification. These results indicate the significant potential for mild stage Alzheimer's disease detection of Alzheimer's disease. Early detection of the disease in the mild stage is a critical factor in preventing the progression of Alzheimer's disease. In addition, a variant of the non -parametric statistical McNemar's Test was applied to determine the statistical significance of the results obtained in the study. Statistical significance of 1 vs 1 and 1 vs all classifications were obtained for EfficientNetB0, DenseNet, and AlexNet models.
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    Fault Detection System For Paper Cup Machine Based On Real-Time Image Processing
    (Başkent Üniversitesi Mühendislik Fakültesi, 2024-03-31) Aydin, Alaaddin; Guney, Selda
    In the production of paper cups in industrial factories, it is tried to print high quality cups with less waste loss with the help of sensors and heating resistances mounted on the paper cup machine. In this study, a system that detects faulty products based on image processing and removes it by controlling the machine with servo motors, asynchronous motors and programmable logic controller (PLC) is designed. For fault product detection, classification has been performed using real-time Haarcascade algorithm and You Only Look Once (YOLO) algorithm which is a deep learning methods, and real-time object detection has been carried out using the OpenCv library. With this study, an effective faulty product detection and removing hardware system was realized by adapting artificial intelligence algorithms to a machine used in industry. Based on the results, a whole system can be applied to systems that involve removing a faulty product from a band in any production, packaging etc. facility is proposed. A hardware consisting of servo motors, asynchronous motors and PLC was designed to separate faulty cups from the existing paper cup production machine in this study. Then, a data set composed of 1068 images was created with images taken from the camera for faulty and faultless paper cups. Using this dataset, the effect of different deep learning methods on performance in the real-time system has been examined and successful results have been obtained. The optimal outcome was achieved, yielding a real-time application accuracy rate of 90.8% through the utilization of the Yolov5x architecture.
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    A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection
    (2023) Oltu, Burcu; Karaca, Busra Kubra; Erdem, Hamit; Ozgur, Atilla; 0000-0002-9237-8347; 0000-0003-1704-1581; AAD-6546-2019
    Diabetic retinopathy, which is extreme visual blindness due to diabetes, has become an alarming issue worldwide. Early and accurate detection of DR is necessary to prevent the progression and reduce the risk of blindness. Recently, many approaches for DR detection have been proposed in the literature. Among them, deep neural networks (DNNs), especially Convolutional Neural Network (CNN) models, have become the most offered approach. However, designing and training new CNN architectures from scratch is a troublesome and labor-intensive task, particularly for medical images. Moreover, it requires training tremendous amounts of parameters. Therefore, transfer learning approaches as pre-trained models have become more prevalent in the last few years. Accordingly, in this study, 43 publications based on DNN and Transfer Learning approaches for DR detection between 2016 and 2021 are reviewed. The reviewed papers are summarized in 4 figures and 10 tables that present detailed information about 29 pre-trained CNN models, 13 DR data sets, and standard performance metrics.
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    Automated Fracture Detection in the Ulna and Radius Using Deep Learning on Upper Extremity Radiographs
    (2023) Erdas, Cagatay Berke; 0000-0003-3467-9923; 37750264
    Objectives: This study aimed to detect single or multiple fractures in the ulna or radius using deep learning techniques fed on upper-extremity radiographs. Materials and methods: The data set used in the retrospective study consisted of different types of upper extremity radiographs obtained from an open-source dataset, with 4,480 images with fractures and 4,383 images without fractures. All fractures involved the ulna or radius. The proposed method comprises two distinct stages. The initial phase, referred to as preprocessing, involved the removal of radiographic backgrounds, followed by the elimination of nonbone tissue. In the second phase, images consisting only of bone tissue were processed using deep learning models, such as RegNetX006, EfficientNet B0, and InceptionResNetV2. Thus, whether one or more fractures of the ulna or the radius are present was determined. To measure the performance of the proposed method, raw images, images generated by background deletion, and bone tissue removal were classified separately using RegNetX006, EfficientNet B0, and InceptionResNetV2 models. Performance was assessed by accuracy, F1 score, Matthew's correlation coefficient, receiver operating characteristic area under the curve, sensitivity, specificity, and precision using 10-fold cross-validation, which is a widely accepted technique in statistical analysis. Results: The best classification performance was obtained with the proposed preprocessing and RegNetX006 architecture. The values obtained for various metrics were as follows: accuracy (0.9921), F1 score (0.9918), Matthew's correlation coefficient (0.9842), area under the curve (0.9918), sensitivity (0.9974), specificity (0.9863), and precision (0.9923). Conclusion: The proposed preprocessing method is able to detect fractures of the ulna and radius by artificial intelligence.
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    Classification of Canine Maturity and Bone Fracture Time Based on X-Ray Images of Long Bones
    (2021) Ergun, Gulnur Begum; Guney, Selda; 0000-0002-0573-1326; 0000-0001-8469-5484
    Veterinarians use X-rays for almost all examinations of clinical fractures to determine the appropriate treatment. Before treatment, vets need to know the date of the injury, type of the broken bone, and age of the dog. The maturity of the dog and the time of the fracture affects the approach to the fracture site, the surgical procedure and needed materials. This comprehensive study has three main goals: determining the maturity of the dogs (Task 1), dating fractures (Task 2), and finally, detecting fractures of the long bones in dogs (Task 3). The most popular deep neural networks are used: AlexNet, ResNet-50 and GoogLeNet. One of the most popular machine learning algorithms, support vector machines (SVM), is used for comparison. The performance of all sub-studies is evaluated using accuracy and F1 score. Each task has been successful with different network architecture. ResNet-50, AlexNet and GoogLeNet are the most successful algorithms for the three tasks, with F1 scores of 0.75, 0.80 and 0.88, respectively. Data augmentation is performed to make models more robust, and the F1 scores of the three tasks were 0.80, 0.81, and 0.89 using ResNet-50, which is the most successful model. This preliminary work can be developed into support tools for practicing veterinarians that will make a difference in the treatment of dogs with fractured bones. Considering the lack of work in this interdisciplinary field, this paper may lead to future studies.