Mühendislik Fakültesi / Faculty of Engineering

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

Browse

search.filters.applied.f.language: search.filters.language.en_USSubject: search.filters.subject.Deep learning

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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.