Sürdürülebilir Kalkınma Amaçları

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• BildiriYalnız künye2025/r/gumushane/14108
  Improving Skin Lesion Classification with Pretrained Deep Learning Models

  Almasani, Heba; Ture, Hayati

  Skin cancer is the most common type of cancer. Cancer can be defined as a disease characterized by the uncontrolled division and spread of cells. As with other types of cancer, early diagnosis and treatment play a crucial role in combating skin cancer. Since diagnosis is made through visual examination, factors such as the level of expertise and image artifacts negatively affect diagnostic accuracy and slow down the process.Computer-aided applications used to analyze such images provide faster and more accurate results. Additionally, these methods eliminate factors that reduce diagnostic accuracy during the classification and segmentation of skin lesions.This study classifies skin cancer to benign or malignant using deep learning and machine learning methods. In particular, Convolutional Neural Networks (CNNs) have shown promising results in medical image classification. The study evaluates six pre-trained CNN models - VGG16, EfficientNetB7, EfficientNetV2L, DenseNet, ConvNeXtBase and MobileNet - for binary skin lesion classification using the ISIC 2019 dataset. The study first preprocesses and balances the data to enhance performance. The results indicate that the ConvNeXtBase model achieved the highest success with accuracy rate of 84.63% after balancing the data that we have.

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  İncele
• BildiriAçık Erişim2024/r/gumushane/16830
  Experimental Demonstration of an Optic-DAC Based Transmitter Structure

  Baser, Oguzhan; Küçük, Mehmet Ali; Albayrak, Cenk; Türk, Kadir

  In this paper, an optical-DAC (ODAC) based transmitter design is proposed for indoor visible light communication (VLC) applications. The proposed design is practically realized and the communication performance of the experimental VLC system is investigated. In the ODAC transmitter, multilevel communication signal can be generated optically by independently switching light emitting diode (LED) groups with different powers. In this way, multilevel modulation techniques in VLC systems can be realized through a simple transmitter structure that can be digitally driven. In the proposed transmitter design, unlike the existing ODAC-based VLC transmitters in the literature, metal oxide semiconductor field effect transistors (MOSFET) are used as switching elements in order to drive high-power LED for ambient illumination and communication. In the experimental studies, an offline VLC system using the designed ODAC-based transmitter with a power of approximately 10 W is tested at different switching speeds and distances. The results show that by implementing 8 PAM modulation in the proposed transmitter design, a data transmission rate of 6.264 Mbps is successfully achieved.

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• BildiriAçık Erişim2024/r/gumushane/16809
  Reconfigurable Optical Filter Based Receiver Structure For Visible Light Communication

  Küçük, Mehmet Ali; Albayrak, Cenk; Türk, Kadir

  In this paper, a novel receiver structure is proposed to mitigate the inter-channel interference (ICI) effects that limit the data rate in multi-color visible light communication (MC-VLC) systems. Reconfigurable optical filters (ROFs) are used at the receiver side of MC-VLC systems to enable the separation of color channels. ROFs are optical elements that have the ability to electrically change their transmission spectrum. Utilizing this feature of ROFs, a receiver structure is developed that, unlike traditional receiver structures, can adaptively adjust the transmit spectrum based on channel state information. This adaptive adjustment is aimed at maximizing communication performance by effectively separating color channels in MC-VLC systems. The obtained results demonstrate that the proposed receiver structure achieves higher communication performance compared to traditional receivers.

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• BildiriAçık Erişim2024/r/gumushane/16231
  Cross-view Gait Recognition Based on Fine-Tuned Deep Networks

  Yaprak, Busranur; Gedikli, Eyup; Bingöl, Ozkan; Dogan, Ramazan Ozgur

  Gait recognition is a biometrics-based computer vision process used to identify people based on their walking styles. Compared to other types of biometrics, gait offers a more advantageous recognition process as it does not require high-resolution and close-range images and obtains without contact. But besides this, gait biometrics is highly affected by cross-view variation, and under this variation recognition performance decreases significantly. In this study, performance evaluations of fine-tuned VGG-16 and ResNet-50 deep CNN networks on the cross-view gait recognition problem are performed. For this purpose, Gait energy images (GEI) and Silhouettes obtained from CASIA-B, the most comprehensive data set in gait recognition, are given as input to the networks. The experimental results showed that the VGG-16 network achieved higher recognition rates in cross-view gait recognition.

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• BildiriAçık Erişim2024/r/gumushane/16073
  A Study on the Segmentation and Classification of Diabetic Retinopathy Images Using the K-Means Clustering Method

  Incir, Ramazan; Bozkurt, Ferhat

  Diabetic Retinopathy (DR) is a retinal disease caused by diabetes, representing one of the most prevalent causes of vision loss affecting millions of people worldwide. Swift detection and treatment of this condition are crucial for preventing the disease. Various deep learning and machine learning algorithms have been employed for disease detection and classification, often overlooking the data preprocessing stage. In the data preprocessing phase of this study, segmentation of important lesions, such as hard exudates, was conducted using the K-Means clustering method. The identified lesions were highlighted on the original images. The resulting dataset was then classified using pre-trained architectures, namely EfficientNetV2-M, ResNet50, MobileNet, and DenseNet121. After training on the APTOS dataset, the EfficientNetV2-M model achieved an accuracy of 95.16%. The classification results indicated the contribution of the lesion highlighting process during data preprocessing to the overall classification accuracy.

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• BildiriYalnız künye2023/r/gumushane/16778
  Shape of Masses in Contrast Mammography Images and First Order Statistical Features Classification Based on

  Kobya, Mehmet Emin; Ture, Hayati; Dogan, Ramazan Ozgur; Kayikcioglu, Temel; Teymur, Aykut; Kul, Sibel

  Mammography is a valuable tool in the diagnosis and staging of primary breast cancer. Contrast-enhanced mammography (CEM) combines an iodinated contrast agent with traditional mammography to increase diagnostic accuracy, especially in women with more dense parenchymal background patterns. In this study, a Decision Support System (DSS) is proposed that classifies masses using radiomic features obtained from masks of benign and malignant masses in images obtained with the CEM technique. An open-access database containing 2006 CEM images was used in this study. Radiomic features obtained from these images were used to identify differences between benign and malignant masses. The DSS uses these features to classify masses using a decision tree algorithm. This study highlights the importance of the use of iodinated contrast agents to improve diagnostic accuracy in women with more dense parenchymal background patterns. Additionally, it emphasizes the potential benefits of a DSS, in combination with radiomic features, in the diagnosis and staging of breast cancer. In conclusion, this study demonstrates that the use of radiomic features in mammography images obtained with the CEM technique is an effective way to classify benign and malignant masses.The use of this technology could be an important tool in the early diagnosis and accurate classification of breast cancer.

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• BildiriYalnız künye2023/r/gumushane/16471
  Deep Learning Based Focus Measurement Operator for 3D Imaging in Microscopic Systems

  Dogan, Hulya; Dogan, Ramazan Ozgur

  Since 3D imaging in microscopic systems makes cell and tissue structures more prominent and visible, it provides higher performance in image processing applications such as automatic segmentation, recognition, classification, alignment and stitching. For this reason, 3D imaging in microscopic systems has been one of the most comprehensive and long-studied research areas in the literature. It seems that the most frequently preferred strategy for 3D imaging is shape from focus. Shape from focus is a strategy that uses a sequence of 2D images with the same perspective and different focused regions to create the 3D shape of the sample. Focus measurement operators are applied to extract the focus properties of the input images in shape from focus strategies. These operators have several limitations, such as more precise characterization of curves and edges in images. Researchers often use a pre- or post-processing technique to minimize these limitations, rather than proposing a new focus measurement operator. In this study, a deep learning based focus measurement operator, which does not require a pre- or post-processing technique, is developed to eliminate these limitations. The qualitative and quantitative results obtained in the study show that the proposed focus measurement operator is more effective in 3D imaging than other operators in the literature.

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• BildiriYalnız künye2023/r/gumushane/16091
  Anomaly Detection in Predicted Water Treatment Data using Hybrid CNN-LSTM Network Model

  Özgenç, Busra; Ayas, Selen; Dogan, Ramazan Ozgur; Çavdar, Bora; Sahin, Ali Klvanc; Ayas, Mustafa Sinasi

  Water treatment systems are among the industrial control systems where it is essential to detect anomalies accurately and efficiently due to the potential threat to public health. With advances in computer science, machine learning models have been successfully used in the anomaly detection process in recent years. In this paper, a hybrid CNN-LSTM network model is proposed to detect anomalies in water systems. Using a statistical window-based anomaly detection approach, the performance of the proposed model in detecting different types of attacks is analyzed on the open SWaT dataset. Experimental results show that the precision, recall and F1-score values of the proposed model are 0.994, 0.973 and 0.983, respectively, and can be successfully used to detect anomalies in water treatment systems.

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  İncele
• BildiriYalnız künye2022/r/gumushane/15989
  Segmentation of Pectoral Muscle Region in MLO Mammography Images by Backboned U-Net

  Dogan, Ramazan Ozgur; Türe, Hayati; Kayikçioglu, Temel

  The pectoral muscle region on MLO mammography images appears prominently similar to suspicious areas. For this reason, Computer-Aided Detection (CAD) systems remove this region to reduce false-positive rates in the mass detection process. In some cases, the pectoral muscle region is exposed to distortions due to the superposition effects caused by the mammography technique. As a result, segmentation error rates of the pectoral muscle region, whose characteristic features are deteriorated, appear. In this study, a method to identify impaired pectoral muscle regions with MobileNetV2 backboned U-Net Deep Learning method is proposed. The proposed method was tested on 84 and 201 mammography images taken from both MIAS and InBreast databases and segmented with 1.81% and 1.92% false-negative (FN) and 0.25% and 0.37% false positive (FP) rates, respectively. Particularly for distorted pectoral muscle regions, the proposed method has been shown to outperform some pioneering studies in this area.

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  İncele
• BildiriYalnız künye2019/r/gumushane/15957
  Meta-Heuristic Algorithms Based Multi-Level Thresholding

  Küçükugurlu, Busranur; Gedikli, Eyup

  Thresholding is a very important stage in computer vision applications. An ideal single thresholding algorithm is not available for all environments. Multi-level thresholding in environments with multiple objects is constantly being developed for interpretation of images. Kapur entropy and Otsu approaches are among the most successful algorithms in the literature. In this study, it is tried to increase the performance of Otsu and Kapur algorithms by using meta-heuristic optimization approaches. The results of the Firefly Algorithm (FF) and Real Coded Genetic Algorithm (RGA) were evaluated with PSNR, SSIM and CPU processing time criteria.

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