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Öğe A hybrid ViT-CNN model with attention mechanism and dual feature selection for cervical cancer detection(Springer London Ltd, 2025) Onal, Merve Kesim; Van, Abdullah Enes; Avci, Derya; Yildirim, Muhammed; Bingol, Harun; Avci, EnginCervical cancer is the most common and fatal disease encountered by women worldwide. Early diagnosis of cervical cancer plays a critical role in reducing mortality rates and initiating the treatment process early. In this study, a hybrid model was developed to detect cervical cancer with high accuracy. In the developed model, 1280 and 768 features were extracted from each image, respectively, using pre-trained EfficientNet V2-M and Vision Transformer (ViT) architectures as the base; these features were combined to obtain a combined feature vector of 2048 dimensions. A feature attention mechanism was applied to highlight the important information in the data input, and then dimensionality reduction was performed using the mRMR and NCA methods. 838 standard features between the features selected with both methods were classified with six different machine learning algorithms. In the study, the five-class public SIPaKMeD dataset was used. The proposed model achieved a high accuracy value of 99.02% on the relevant dataset. In order to compare the performances of the models used in the study, different metrics such as Accuracy, Recall, Precision and F1 Score were evaluated. The proposed model was also compared with the performances of 8 different Convolutional Neural Network's (CNN's) and 4 different ViT architectures accepted in the literature. The proposed model produced more successful results than traditional approaches and similar studies in the literature.Öğe A new super resolution Faster R-CNN model based detection and classification of urine sediments(Elsevier, 2023) Avci, Derya; Sert, Eser; Dogantekin, Esin; Yildirim, Ozal; Tadeusiewicz, Ryszard; Plawiak, PawelThe diagnosis of urinary tract infections and kidney diseases using urine microscopy images has gained significant attention of medical community in recent years. These images are usually created by physicians' own rule of thumb manually. However, this man-ual urine sediment analysis is usually labor-intensive and time-consuming. In addition, even when physicians carefully examine an image, an erroneous cell recognition may occur due to some optical illusions. In order to achieve cell recognition in low-resolution urine microscopy images with a higher level of accuracy, a new super resolution Faster Region-based Convolutional Neural Network (Faster R-CNN) method is proposed. It aims to increase resolution in low-resolution urine microscopy images using self-similarity based single image super resolution which was used during the pre-processing. De-noising based Wiener filter and Discrete Wavelet Transform (DWT) are used to de-noise high resolution images, respectively, to increase the level of accuracy for image recognition. Finally, for the feature extraction and classification stages, AlexNet, VGFG16 and VGG19 based Faster R-CNN models are used for the recognition and detection of multi-class cells. The model yielded accuracy rates are 98.6%, 96.4% and 96.2% respectively.(c) 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.Öğe MFIF-DWT-CNN: Multi-focus image fusion based on discrete wavelet transform with deep convolutional neural network(Springer, 2024) Avci, Derya; Sert, Eser; Ozyurt, Fatih; Avci, EnginA new fusion method based on Multi-Focus Image Fusion Based on Discrete Wavelet Transform with Deep Convolutional Neural Network (MFIF-DWT-CNN) is presented to reduce spatial artifacts and blurring effects in edge details and increase the robustness of multifocal image fusion. The main purpose of the MFIF-DWT-CNN approach is to create a new merged image by collecting the required features from the main image. With the MFIF-DWT-CNN approach, information focused on individual images is combined into a single image, resulting in a clearer image. Within the scope of MFIF-DWT-CNN approach, DWT is applied to the image pairs and the obtained images are then given to the CNN architecture. The MFIF-DWT-CNN approach was developed in this study to reduce spatial artifacts and blurring effects in edge details and to increase the robustness of multifocal image fusion. In order to evaluate our proposed MFIF-DWT-CNN method, QMI, QG, QYi QCB evaluations were made on the public data set. From the experimental results, it is seen that the proposed method gives better results in the relevant metrics than the other methods. This demonstrated the effectiveness of the proposed method.Öğe Termal Kamera Görüntülerinin Çoklu Sınıflandırılması için Derin Öğrenme Tabanlı Tekniklerin Performans Karşılaştırılması(2025) ÖNAL, MERVE KESİM; USLU, Halil; Avci, Engin; Avci, DeryaTermal kameralar, cisimlerin sıcaklık farklılıklarını kızılötesi ışın değerlerine bağlı olarak renklendirdiği görüntüleme sistemleridir. Günümüzde başta savunma sanayi olmak üzere sağlık, ziraat, inşaat gibi birçok farklı alanda termal kameralar kullanılmaktadır. Özellikle savunma sanayi alanında kullanılan bu kameralardan elde edilen görüntüler, çeşitli nesnelerin ve canlıların tespiti için büyük önem arz etmektedir. Bu çalışmada termal kamera görüntülerinin sınıflandırması için derin öğrenme tabanlı tekniklerin kapsamlı bir karşılaştırması sunulmaktadır. Çalışmada 7 farklı Evrişimli Sinir Ağları mimarisi ile görüntülerin özellikleri çıkarılmış, 5 farklı sınıflandırma yöntemi ile sınıflandırılması sağlanmıştır. Performans değerlendirmesi için dengesiz çok sınıflı veri kümelerinin sınıflandırılmasına uygun metrikler olan dengeli doğruluk, makro ve mikro ortalama duyarlılık, makro ve mikro ortalama kesinlik, makro ve mikro ortalama F ölçütü metrik değerleri kullanılmıştır. Ayrıca tüm yapıların ayrı ayrı eğitim ve test süreleri karşılaştırılmıştır. Çalışmada en yüksek doğruluk değeri %95.24 ile Resnet101+Softmax ve Resnet50+DVM mimarilerinde elde edilmiştir. Sınıfların eşit ağırlıklı alındığı dengeli doğruluk değerinde ise en yüksek %95,17 ile Resnet101+Softmax mimarisinden elde edilmiştir. Resnet101+Softmax mimarisinde makro ortalamalı kesinlik 0.9579, makro ortalamalı F ölçütü 0.9543 ve mikro ortalamalı F ölçütü 0.9524 değerleri elde edilmiştir. Bu çalışma, küçük ve dengesiz termal görüntüler üzerinde, önceden eğitilmiş ESA ağlarının özellik çıkarımı ile makine öğrenimi sınıflandırıcılarının kullanımının, tamamen eğitilmiş ağlarla elde edilen performansa benzer sonuçlar sağlanabileceğini göstermiştir
