Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
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Item Automated Detection of type 1 ROP, type 2 ROP and A-ROP Based on Deep Learning(Başkent Üniversitesi Mühendislik Fakültesi, 2024-07-02) Yenice, Esay Kiran; Kara, Caner; Erdas, Cagatay BerkePurposeTo provide automatic detection of Type 1 retinopathy of prematurity (ROP), Type 2 ROP, and A-ROP by deep learning-based analysis of fundus images obtained by clinical examination using convolutional neural networks.Material and methodsA total of 634 fundus images of 317 premature infants born at 23-34 weeks of gestation were evaluated. After image pre-processing, we obtained a rectangular region (ROI). RegNetY002 was used for algorithm training, and stratified 10-fold cross-validation was applied during training to evaluate and standardize our model. The model's performance was reported as accuracy and specificity and described by the receiver operating characteristic (ROC) curve and area under the curve (AUC).ResultsThe model achieved 0.98 accuracy and 0.98 specificity in detecting Type 2 ROP versus Type 1 ROP and A-ROP. On the other hand, as a result of the analysis of ROI regions, the model achieved 0.90 accuracy and 0.95 specificity in detecting Stage 2 ROP versus Stage 3 ROP and 0.91 accuracy and 0.92 specificity in detecting A-ROP versus Type 1 ROP. The AUC scores were 0.98 for Type 2 ROP versus Type 1 ROP and A-ROP, 0.85 for Stage 2 ROP versus Stage 3 ROP, and 0.91 for A-ROP versus Type 1 ROP.ConclusionOur study demonstrated that ROP classification by DL-based analysis of fundus images can be distinguished with high accuracy and specificity. Integrating DL-based artificial intelligence algorithms into clinical practice may reduce the workload of ophthalmologists in the future and provide support in decision-making in the management of ROP.Item Computer-Aided Colorectal Cancer Diagnosis: Ai-Driven Image Segmentation And Classification(Başkent Üniversitesi Mühendislik Fakültesi, 2024-05-31) Erdas, Cagatay BerkeColorectal 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.Item Automated Fracture Detection in the Ulna and Radius Using Deep Learning on Upper Extremity Radiographs(2023) Erdas, Cagatay Berke; 0000-0003-3467-9923; 37750264Objectives: 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.Item CNN-Based Severity Prediction Of Neurodegenerative Diseases Using Gait Data(2022) Erdas, Cagatay Berke; Sumer, Emre; Kibaroglu, Seda; https://orcid.org/0000-0003-3467-9923; 35111334; AGA-5711-2022Neurodegenerative diseases occur because of degeneration in brain cells but can manifest as impairment of motor functions. One of the side effects of this impairment is an abnormality in walking. With the development of sensor technologies and artificial intelligence applications in recent years, the disease severity of patients can be estimated using their gait data. In this way, decision support applications for grading the severity of the disease that the patient suffers in the clinic can be developed. Thus, patients can have treatment methods more suitable for the severity of the disease. The presented research proposes a deep learning-based approach using gait data represented by a Quick Response code to develop an effective and reliable disease severity grading system for neurodegenerative diseases such as amyotrophic lateral sclerosis, Huntington's disease, and Parkinson's disease. The two-dimensional Quick Response data set was created by converting each one-dimensional gait data of the subjects with a novel representation approach to a Quick Response code. This data set was regressed with the convolutional neural network deep learning method, and a solution was sought for the problem of grading disease severity. Further, to demonstrate the success of the results obtained with the novel approach, native machine learning approaches such as Multilayer Perceptron, Random Forest, Extremely Randomized Trees, and K-Nearest Neighbours, and ensemble machine learning methods, such as voting and stacking, were applied on one-dimensional data. Finally, the results obtained on the prediction of disease severity by testing one-dimensional gait data with a convolutional neural network architecture that operates on one-dimensional data were included. The results showed that, in most cases, the two-dimensional convolutional neural network approach performed the best among all methods.