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Öğe A novel deep learning-based perspective for tooth numbering and caries detection(Springer Heidelberg, 2024) Ayhan, Baturalp; Ayan, Enes; Bayraktar, YusufObjectivesThe aim of this study was automatically detecting and numbering teeth in digital bitewing radiographs obtained from patients, and evaluating the diagnostic efficiency of decayed teeth in real time, using deep learning algorithms.MethodsThe dataset consisted of 1170 anonymized digital bitewing radiographs randomly obtained from faculty archives. After image evaluation and labeling process, the dataset was split into training and test datasets. This study proposed an end-to-end pipeline architecture consisting of three stages for matching tooth numbers and caries lesions to enhance treatment outcomes and prevent potential issues. Initially, a pre-trained convolutional neural network (CNN) utilized to determine the side of the bitewing images. Then, an improved CNN model YOLOv7 was proposed for tooth numbering and caries detection. In the final stage, our developed algorithm assessed which teeth have caries by comparing the numbered teeth with the detected caries, using the intersection over union value for the matching process.ResultsAccording to test results, the recall, precision, and F1-score values were 0.994, 0.987 and 0.99 for teeth detection, 0.974, 0.985 and 0.979 for teeth numbering, and 0.833, 0.866 and 0.822 for caries detection, respectively. For teeth numbering and caries detection matching performance; the accuracy, recall, specificity, precision and F1-Score values were 0.934, 0.834, 0.961, 0.851 and 0.842, respectively.ConclusionsThe proposed model exhibited good achievement, highlighting the potential use of CNNs for tooth detection, numbering, and caries detection, concurrently.Clinical significanceCNNs can provide valuable support to clinicians by automating the detection and numbering of teeth, as well as the detection of caries on bitewing radiographs. By enhancing overall performance, these algorithms have the capacity to efficiently save time and play a significant role in the assessment process.Öğe Ağırlıklandırılmış Evrişimsel Sinir Ağları Topluluğu ile Göğüs Radyografilerinden Kardiyomegali Tespiti(Kırıkkale Üniversitesi, 2024) Ayan, EnesKardiyomegali bir hastalık olmamasına karşın birçok kalp rahatsızlığının belirtisi olarak ortaya çıkabilmektedir. Bu belirtinin erken teşhis edilip altında yatan sebeplerin araştırılması hasta için hayati bir önem arz etmektedir. Kardiyomegali teşhisi için en sık kullanılan yöntemlerden biri göğüs radyografisidir. Derin öğrenme yöntemleri ile radyografik görüntülerin analizi son yıllarda oldukça popüler bir çalışma alanıdır. Özellikle evrişimsel sinir ağları medikal görüntü analizinde başarılı sonuçlar elde etmiştir. Bu çalışmada hekimlerin göğüs radyografilerini analiz ederken ikinci bir görüş alabilecekleri, göğüs radyografilerini normal ve kardiyomegali olmak üzere sınıflandıracak ağırlıklandırılmış evrişimsel sinir ağı (ESA) topluluğu önerilmiştir. Bu bağlamda kardiyomegali tespit etmesi için eğitilen on ESA modeli arasından en başarılı üç model ağırlıklandırılmış topluluk yöntemi için seçilmiştir. Seçilen modellerin ağırlıkları parçacık sürü optimizasyon algoritması kullanılarak belirlenmiştir. Elde edilen ağırlıklar kullanılarak yapılan testler sonucunda önerilen yöntem %89,09 doğruluk %89,09 duyarlılık, %89,30 kesinlik ve %89,08 F1 skor değerleri elde etmiştir.Öğe Classification of Gastrointestinal Diseases in Endoscopic Images: Comparative Analysis of Convolutional Neural Networks and Vision Transformers(2024) Ayan, EnesGastrointestinal (GI) diseases are a major issue in the human digestive system. Therefore, many studies have explored the automatic classification of GI diseases to reduce the burden on clinicians and improve patient outcomes for both diagnosis and treatment purposes. Convolutional neural networks (CNNs) and Vision Transformers (ViTs) in deep learning approaches have become a popular research area for the automatic detection of diseases from medical images. This study evaluated the classification performance of thirteen different CNN models and two different ViT architectures on endoscopic images. The impact of transfer learning parameters on classification performance was also observed. The tests revealed that the classification accuracies of the ViT models were 91.25% and 90.50%, respectively. In contrast, the DenseNet201 architecture, with optimized transfer learning parameters, achieved an accuracy of 93.13%, recall of 93.17%, precision of 93.13%, and an F1 score of 93.11%, making it the most successful model among all the others. Considering the results, it is evident that a well-optimized CNN model achieved better classification performance than the ViT models.Öğe Çok Ölçütlü Karar Verme Yöntemleri ile Ekokardiyografi Cihazı Seçiminin Yapılması(2017) Cihan, Şeyma; Ayan, Enes; Eren, Tamer; Topal, Taner; Yıldırım, Erdem KamilAmaç: Medikal cihazlar hastanelerde en çok bütçe ayrılan kalemlerdendir. Bu nedenle medikal cihaz alım kararı kritik kararlardan biridir. Bu çalışmada, bir devlet hastanesinde Kardiyoloji Servisine alınması planlanan ekokardiyografi cihazı seçim problemi ele alınmıştır. Gereç ve Yöntem: Ekokardiyografi cihazı seçimini etkileyen kriterler ve mevcut alternatif cihazlar konuyla ilgili literatür taranarak ve kardiyoloji alanında uzman üç hekimin görüşleri alınarak belirlenmiştir. Seçim kriterleri belirlendikten sonraAnalitik Hiyerarşi Süreci (AHP) yöntemi ile kriterler ağırlıklandırılmış ve İdeal Çözüme Dayalı Sıralama Tekniği (TOPSIS) yöntemi ile alternatifler sıralanarak en iyi alternatif belirlenmiştir. Bulgular: Ekokardiyografi cihazının seçim problemi için belirlenen alternatiflerin öncelikleri AHP yöntemine göre, B cihazı için %48, ikinci sırada yer alan A cihazı için %29, C cihazı için ise %23 olarak bulunmuştur. TOPSIS yöntemi ile İdeal Uzaklık (Si* ), Negatif İdeal Uzaklık (Si-) ve her bir karar noktasının ideal çözüme göreli yakınlık (Ci*) değerleri B cihazı için sırasıyla 0, 0.124287 ve 1 olarak bulunmuştur. Sonuç: AHP ve TOPSIS yöntemine göre B cihazı birinci alternatif olarak öne çıkmıştırÖğe Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks(ELSEVIER SCI LTD, 2020) Ayan, Enes; Erbay, Hasan; Varcin, FatihInsects are among the important causes of significant losses in crops such as rice, wheat, corn, soybeans, sugarcane, chickpeas, potatoes. Identification of insect species in the early period is crucial so that the necessary precautions can be taken to keep losses at a low level. However, accurate identification of various types of crop insects is a challenging task for the farmers due to the similarities among insect species and also their lack of knowledge. To address this problem, computerized methods, especially based on Convolutional Neural Networks (CNNs), can be employed. CNNs have been used successfully in many image classification problems due to their ability to learn data-dependent features automatically from the data. Throughout the study, seven different pre-trained CNN models (VGG-16, VGG-19, ResNet-50, Inception-V3, Xception, MobileNet, SqueezeNet) were modified and re-trained using appropriate transfer learning and finetuning strategies on publicly available D0 dataset with 40 classes. Later, the top three best performing CNN models, Inception-V3, Xception, and MobileNet, were ensembled via sum of maximum probabilities strategy to increase the classification performance, the model was named SMPEnsemble. After that, these models were ensembled using weighted voting. The weights were determined by the genetic algorithm that takes the success rate and predictive stability of three CNN models into account, the model was named GAEnsemble. GAEnsemble achieved the highest classification accuracy of 98.81% for D0 dataset. For the sake of robustness ensembled model, without changing the initial best performing CNN models on D0, the process was repeated by using two more datasets such that SMALL dataset with 10 classes and IP102 dataset with 102 classes. The accuracy values for GAEnsemble are 95.15% for SMALL dataset and 67.13% for IP102. In terms of performance metrics, GAEnsemble is competitive compared to the literature for each of these three datasets.Öğe Dental student application of artificial intelligence technology in detecting proximal caries lesions(Wiley, 2024) Ayan, Enes; Bayraktar, Yusuf; Celik, Cigdem; Ayhan, BaturalpObjectives: This study aimed to investigate the caries diagnosis performances of dental students after training with an artificial intelligence (AI) application utilizing deep learning techniques, a type of artificial neural network.Methods: A total of 1200 bitewing radiographs were obtained from the institution's database and two specialist dentists labeled the caries lesions in the images. Randomly selected 1000 images were used for training purposes and the remaining 200 radiographs were used to evaluate the caries diagnostic performance of the AI. Then, a convolutional neural network, a deep learning algorithm commonly employed to analyze visual imagery problems, called You Only Look Once, was modified and trained to detect enamel and dentin caries lesions in the radiographs. Forty dental students were selected voluntarily and randomly divided into two groups. The pre-test results of dental caries diagnosis performances of both groups were recorded. After 1 week, group 2 students were trained using an AI application. Then, the post-test results of both groups were recorded. The labeling duration of the students was also measured and analyzed.Results: When both groups' pre-test and post-test results were evaluated, a statistically significant improvement was found for all parameters examined except precision score (p < 0.05). However, the trained group's accuracy, sensitivity, specificity, and F1 scores were significantly higher than the non-trained group in terms of post-test scores (p < 0.05). In group 2 (trained group), the post-test labeling time was considerably increased (p < 0.05).Conclusions: The students trained by AI showed promising results in detecting caries lesions. The use of AI can also contribute to the clinical education of dental students.Öğe Dermoskopik görüntülerden melanomanın derin evrişimsel sinir ağları ile teşhisi(Kırıkkale Üniversitesi, 2019) Ayan, Enes; Ünver, Halil MuratDeri kanseri dünya genelinde son yıllarda oldukça sık karşılaşılan bir halk sağlığı sorunudur. Güneş ışınlarının zararlı etkisi sonucunda onarılamayan DNA hasarına bağlı olarak deri hücrelerinin kontrolsüz büyümesi ile ortaya çıkmaktadır. Farklı türleri bulunan deri kanserinin en tehlikelisi olan ve insan yaşamını tehdit eden türü melanomadır. Diğer deri kanserlerinin yayılma kapasiteleri sınırlı iken, melanomanın esas tehlikesi çok hızlı yayılmasıdır. Neyse ki melanoma erken teşhis edildiğinde %99 oranında tedavi edilebilir bir hastalıktır. Hastalığın teşhisi için dermoskop cihazı ile elde edilen dermoskopik görüntüler kullanılmaktadır. Hekimler tarafından incelenen görüntüler üzerinden elde edilen bilgiler ışığında lezyon şüpheli görülürse biyopsi yapılarak kesin teşhis konulmaktadır. Teşhis başarısı çoğunlukla hekim deneyimine bağlı olmakla birlikte özneldir. Yanlış teşhisler sonucu gereksiz biyopsi sayılarında artış görülmektedir. Ayrıca hastalığın geç teşhis edilmesi de ortaya çıkan olumsuz durumlardan bir tanesidir. Bu nedenle, güvenilir otomatik melanoma tarama sistemleri, hekimlerin kötü huylu cilt lezyonlarını mümkün olduğunca erken tespit etmeleri için çok yardımcı olacaktır. Son beş yıl içinde derin öğrenme yöntemleri klasik görüntü işleme metotlarını geride bırakarak sınıflandırma problemlerinde büyük başarılar elde etmiştir. Özellikle evrişimsel sinir ağları medikal görüntüler üzerinden birçok hastalığın teşhisini başarı ile gerçekleştirmiştir. Bununla birlikte derin öğrenme yöntemlerinin başarım oranı kullanılan veri setinin büyüklüğü ile doğru orantılıdır. Bu çalışmada derin öğrenme yöntemlerinden evrişimsel sinir ağları vasıtası ile dermoskopik görüntülerden oluşan kısıtlı bir veri seti kullanılarak melanom olan lezyonların tespitine odaklanılmıştır. Bu amaçla üç aşamadan oluşan bir boru hattı mimarisi oluşturulmuştur. Önerilen mimarinin ilk aşaması lezyon üzerindeki kılların tespiti ve yok edilmesidir. Bu aşamada aktarım öğrenme yönteminden faydalanılarak Vgg tabanlı bir evrişim ağı ile üzerinde kıl olan lezyon tespit edildikten sonra üzerindeki kıllar çeşitli görüntü işleme yöntemleri ile temizlenmiştir. İkinci aşamada lezyonun sağlıklı dokudan ayrılmasıdır. Bu aşamada Yolov3 derin ağı görüntü içinde lezyonun bulunduğu bölgeyi tespit için düzenlenerek yeniden eğitilmiştir. Elde edilen yer bilgisi GrabCut algoritmasında kullanılarak lezyon bölgesi arka plandan ayrılmıştır. Son aşama ise lezyonun sınıflandırılmasıdır. Bu aşamada MobileNet, ResNet-50, Xception ağları aynı veri üzerinde ayrı ayrı eğitilerek test aşamasında oylama yöntemi ile sınıflandırma gerçekleştirilmiştir. Önerilen yöntemin ilk aşaması olan kılları tespit etme silmede %98 hassasiyet elde edilmiştir. Segmentasyon kısmında %90 hassasiyet ve en son sınıflandırmada ise %91 hassasiyet elde edilmiştir.Öğe Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographs(Springer Heidelberg, 2022) Bayraktar, Yusuf; Ayan, EnesObjectives This study aimed to investigate the effectiveness of deep convolutional neural network (CNN) in the diagnosis of interproximal caries lesions in digital bitewing radiographs. Methods and materials A total of 1,000 digital bitewing radiographs were randomly selected from the database. Of these, 800 were augmented and annotated as decay by two experienced dentists using a labeling tool developed in Python programming language. The 800 radiographs were consisted of 11,521 approximal surfaces of which 1,847 were decayed (lesion prevalence for train data was 16.03%). A CNN model known as you only look once (YOLO) was modified and trained to detect caries lesions in bitewing radiographs. After using the other 200 radiographs to test the effectiveness of the proposed CNN model, the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC) were calculated. Results The lesion prevalence for test data was 13.89%. The overall accuracy of the CNN model was 94.59% (94.19% for premolars, 94.97% for molars), sensitivity was 72.26% (75.51% for premolars, 68.71% for molars), specificity was 98.19% (97.43% for premolars, 98.91% for molars), PPV was 86.58% (83.61% for premolars, 90.44% for molars), and NPV was 95.64% (95.82% for premolars, 95.47% for molars). The overall AUC was measured as 87.19%. Conclusions The proposed CNN model showed good performance with high accuracy scores demonstrating that it could be used in the diagnosis of caries lesions in bitewing radiographs. Clinical significance Correct diagnosis of dental caries is essential for a correct treatment procedure. CNNs can assist dentists in diagnosing approximal caries lesions in bitewing radiographs.Öğe Diagnosis of Pediatric Pneumonia with Ensemble of Deep Convolutional Neural Networks in Chest X-Ray Images(Springer Heidelberg, 2022) Ayan, Enes; Karabulut, Bergen; Unver, Halil MuratPneumonia is a fatal disease that appears in the lungs and is caused by viral or bacterial infection. Diagnosis of pneumonia in chest X-ray images can be difficult and error-prone because of its similarity with other infections in the lungs. The aim of this study is to develop a computer-aided pneumonia detection system to facilitate the diagnosis decision process. Therefore, a convolutional neural network (CNN) ensemble method was proposed for the automatic diagnosis of pneumonia which is seen in children. In this context, seven well-known CNN models (VGG-16, VGG-19, ResNet-50, Inception-V3, Xception, MobileNet, and SqueezeNet) pre-trained on the ImageNet dataset were trained with the appropriate transfer learning and fine-tuning strategies on the chest X-ray dataset. Among the seven different models, the three most successful ones were selected for the ensemble method. The final results were obtained by combining the predictions of CNN models with the ensemble method during the test. In addition, a CNN model was trained from scratch, and the results of this model were compared with the proposed ensemble method. The proposed ensemble method achieved remarkable results with an AUC of 95.21 and a sensitivity of 97.76 on the test data. Also, the proposed ensemble method achieved classification accuracy of 90.71 in chest X-ray images as normal, viral pneumonia, and bacterial pneumonia.Öğe Diagnosis of Pneumonia from Chest X-Ray Images using Deep Learning(Ieee, 2019) Ayan, Enes; Unver, Halil MuratPneumonia is a disease which occurs in the lungs caused by a bacterial infection. Early diagnosis is an important factor in terms of the successful treatment process. Generally, the disease can be diagnosed from chest X-ray images by an expert radiologist. The diagnoses can be subjective for some reasons such as the appearance of disease which can be unclear in chest X-ray images or can be confused with other diseases. Therefore, computer-aided diagnosis systems are needed to guide the clinicians. In this study, we used two well-known convolutional neural network models Xception and Vgg16 for diagnosing of pneumonia. We used transfer learning and fine-tuning in our training stage. The test results showed that Vgg16 network exceed Xception network at the accuracy with 0.87%, 0.82% respectively. However, the Xception network achieved a more successful result in detecting pneumonia cases. As a result, we realized that every network has own special capabilities on the same dataset.Öğe Genetic Algorithm-Based Hyperparameter Optimization for Convolutional Neural Networks in the Classification of Crop Pests(Springer Heidelberg, 2024) Ayan, EnesCrop pest classification is essential for a strong and sustainable agricultural economy and food safety. However, the classification of pests is a time-consuming process that requires domain knowledge and relies on expertise. Therefore, automation of the classification process can reduce cost, improve accuracy, and facilitate analysis. In recent years convolutional neural networks (CNNs) and transfer learning fine-tuning methods have gained popularity in solving many computer vision problems in agriculture. The main advantage of pre-trained CNN models is instead of designing and training a model from scratch to solve various classification problems utilizing pre-trained models via transfer learning fine-tuning methods. However, it is important to determine transfer learning and fine-tuning hyperparameters of a pre-trained CNN model to achieve a successful classification performance. But this is a challenging task that requires experience, knowledge, and a lot of effort. This study proposed a new genetic algorithm-based hyperparameter optimization strategy for pre-trained CNN models in insect pest type classification. The proposed method was tested with three CNN models at different scales (MobileNetV2, DenseNet121, and InceptionResNetV2) on three insect datasets; Deng's dataset with 10 classes, Xie2's dataset named D0 with 40 classes, and Wu's dataset named IP102 with 102 classes. The optimized CNN models have achieved state-of-the-art accuracies on D0 (99.89%) and Deng (97.58%) datasets and showed the closest performance to the literature on the IP102 (71.84%) dataset. According to the test results, the proposed method effectively classifies various crop pests and can be used in farming to save crop fields.Öğe Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm(Mdpi, 2019) Unver, Halil Murat; Ayan, EnesSkin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index.Öğe Using a Convolutional Neural Network as Feature Extractor for Different Machine Learning Classifiers to Diagnose Pneumonia(2022) Ayan, EnesPneumonia is a general public health problem. It is an important risk factor, especially for children under 5 years old and people aged 65 and older. Fortunately, it is a treatable disease when diagnosed in the early phase. The most common diagnostic method known for the disease is chest X-Rays. However, the disease can be confused with different disorders in the lungs or its variants by experts. In this context, computer-aided diagnostic systems are necessary to provide a second opinion to experts. Convolutional neural networks are a subfield in deep learning and they have demonstrated success in solving many medical problems. In this paper, Xception which is a convolutional neural network was trained with the transfer learning method to detect viral pneumonia, normal cases, and bacterial pneumonia in chest X-Rays. Then, five different machine learning classification algorithms were trained with the features obtained by the trained convolutional neural network. The classification performances of the algorithms were compared. According to the test results, Xception achieved the best classification result with an accuracy of 89.74%. On the other hand, SVM achieved the closest classification performance to the convolutional neural network model with 89.58% accuracy.Öğe Yapay zekâ ve nano-topaklar: Genetik algoritma uygulaması(Kırıkkale Üniversitesi, 2015) Ayan, Enes; Yıldırım, Erdem KamilTeorik ve deneysel olarak nano topaklar son yıllarda oldukça fazla ilgi çekmeye başladı. Benzer bulk materyallerle karşılaştırıldığında çok farklı kimyasal ve fiziksel özelliklere sahip olabilmektedirler. Nano topak yapıları nano malzemelerin temel taşları olduklarından geometrilerinin ve kararlı yapılarının bulunması oldukça önemlidir. Nano materyallerin istikrarlı geometrilerini tahmin etmek için, araştırmacılar farklı yöntemler kullanmışlardır bunlardan bazıları; monte carlo, moleküler dinamik, rassal arama metotları, genetik algoritmalar ve benzetim tavlama algoritmasıdır. Bu çalışmada, tek tip atomdan oluşan topak yapılarının istikrarlı geometrilerini tahmin etmek için daha önce geliştirilmiş genetik algoritma kodlarından ayrı olarak çaprazlama ve mutasyon operatörleri farklı genetik algoritma kodu geliştirildi. Geliştirilen kod DFT (Yoğunluk Fonksiyon Teorisi) ve genetik algoritmayı birlikte kullanan örneklerden bir tanesidir. Bu kodun doğru çalıştığını doğrulamak amacıyla B4, B5, B6, B8, Li5, Li6, Li7 topakları üzerinde test edilerek literatürdeki sonuçlar ile karşılaştırıldı. Elde edilen sonuçlar geliştirilen kodun çalıştığımız yapıların kararlı geometrilerini başarılı bir şekilde tahmin ettiğini ortaya koymuştur. Buna ek olarak geliştirilen kod hesaplama sürecindeki farklılıklardan dolayı daha önceki genetik algoritma kodlarından daha hızlı çalışmaktadır.
