Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2023/r/gumushane/12780
  Detection of COVID-19 infection from CT images using the medical photogrammetry technique

  Çatal Reis, Hatice; Türk, Veysel; Kaya, Serhat

  Medical data such as computed tomography (CT), magnetic resonance imaging (MRI), and Ultrasound images are used in medical photogrammetry. CT images have been used frequently in recent years for the diagnosis of COVID-19 disease, which has contagious and fatal symptoms. CT is an effective method for early detection of lung anomalies due to COVID-19 infection. Machine learning (ML) techniques can be used to detect and diagnose medical diseases. In particular, classification methods are applied for disease diagnosis and diagnosis. This study proposes traditional machine learning algorithms Random Forest, Logistic Regression, K-Nearest Neighbor and Naive Bayes, and an ensemble learning model to detect COVID-19 anomalies using CT images. According to the experimental findings, the proposed ensemble learning model produced an accuracy of 96.71%. This study can help identify the fastest and most accurate algorithm that predicts CT images with Covid-19 during the epidemic process. In addition, machine learning-based approaches can support healthcare professionals and radiologists in the diagnostic phase.

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• MakaleYayıncıda açık erişim2023/r/gumushane/12018
  MediNet: transfer learning approach with MediNet medical visual database

  Reis, Hatice Catal; Turk, Veysel; Khoshelham, Kourosh; Kaya, Serhat

  The rapid development of machine learning has increased interest in the use of deep learning methods in medical research. Deep learning in the medical field is used in disease detection and classification problems in the clinical decision-making process. Large amounts of labeled datasets are often required to train deep neural networks; however, in the medical field, the lack of a sufficient number of images in datasets and the difficulties encountered during data collection are among the main problems. In this study, we propose MediNet, a new 10-class visual dataset consisting of Rontgen (X-ray), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, and Histopathological images such as calcaneal normal, calcaneal tumor, colon benign colon adenocarcinoma, brain normal, brain tumor, breast benign, breast malignant, chest normal, chest pneumonia. AlexNet, VGG19-BN, Inception V3, DenseNet 121, ResNet 101, EfficientNet B0, Nested-LSTM + CNN, and proposed RdiNet deep learning algorithms are used in the transfer learning for pre-training and classification application. Transfer learning aims to apply previously learned knowledge in a new task. Seven algorithms were trained with the MediNet dataset, and the models obtained from these algorithms, namely feature vectors, were recorded. Pre-training models were used for classification studies on chest X-ray images, diabetic retinopathy, and Covid-19 datasets with the transfer learning technique. In performance measurement, an accuracy of 94.84% was obtained in the traditional classification study for the InceptionV3 model in the classification study performed on the Chest X-Ray Images dataset, and the accuracy was increased 98.71% after the transfer learning technique was applied. In the Covid-19 dataset, the classification success of the DenseNet121 model before pre-trained was 88%, while the performance after the transfer application with MediNet was 92%. In the Diabetic retinopathy dataset, the classification success of the Nested-LSTM + CNN model before pre-trained was 79.35%, while the classification success was 81.52% after the transfer application with MediNet. The comparison of results obtained from experimental studies observed that the proposed method produced more successful results. Graphical abstract: [Figure not available: see fulltext.]

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  İncele
• MakaleYalnız künye2022/r/gumushane/11535
  InSiNet: a deep convolutional approach to skin cancer detection and segmentation

  Reis, Hatice Catal; Turk, Veysel; Khoshelham, Kourosh; Kaya, Serhat

  Cancer is among the common causes of death around the world. Skin cancer is one of the most lethal types of cancer. Early diagnosis and treatment are vital in skin cancer. In addition to traditional methods, method such as deep learning is frequently used to diagnose and classify the disease. Expert experience plays a major role in diagnosing skin cancer. Therefore, for more reliable results in the diagnosis of skin lesions, deep learning algorithms can help in the correct diagnosis. In this study, we propose InSiNet, a deep learning-based convolutional neural network to detect benign and malignant lesions. The performance of the method is tested on International Skin Imaging Collaboration HAM10000 images (ISIC 2018), ISIC 2019, and ISIC 2020, under the same conditions. The computation time and accuracy comparison analysis was performed between the proposed algorithm and other machine learning techniques (GoogleNet, DenseNet-201, ResNet152V2, EfficientNetB0, RBF-support vector machine, logistic regression, and random forest). The results show that the developed InSiNet architecture outperforms the other methods achieving an accuracy of 94.59%, 91.89%, and 90.54% in ISIC 2018, 2019, and 2020 datasets, respectively. Since the deep learning algorithms eliminate the human factor during diagnosis, they can give reliable results in addition to traditional methods. Graphical abstract: [Figure not available: see fulltext.]

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  İncele
• MakaleAçık Erişim2022/r/gumushane/11422
  Automatic prediction of covid-19 from chest-computed tomography (CT) images using deep learning architectures; [Derin öğrenme mimarilerini kullanarak göğüs BT görüntülerinden otomatik Covid-19 tahmini]

  Turk, Veysel; Catal Reis, Hatice; Kaya, Serhat

  Machine learning has been actively used in disease detection and segmentation in recent years. For the last few years, the world has been coping with the Coronavirus disease 2019 (COVID-19) pandemic. Chest-computerized tomography (CT) is often a meaningful way to detect and detect patients with possible COVID-19. This study aims to classify COVID-19 and non-COVID-19 chest-CT images using deep learning (DL) algorithms and investigate whether we can achieve successful results in different parameters using four architectures. The study was performed on proved positive COVID-19 CT images, and the datasets were obtained from the GitHub public platform. The study evaluated four different deep learning architectures of VGG16, VGG19, LeNet-5, and MobileNet. The performance evaluations were used with ROC curve, recall, accuracy, F1-score, precision, and Root Mean Square Error (RMSE). MobileNet model showed the best result ; F1 score of 95%, the accuracy of 95%, the precision of 100%, recall of 90%, AUC of 95%, and RMSE of 0.23. On the other hand, VGG 19 model gave the lowest performance; F1 score of 90%, the accuracy of 89%, the precision of 90%, recall of 90%, AUC of 89%, and RMSE of 0.32. When the algorithms' performances were compared, the highest accuracy was obtained from MobileNet, LeNet-5, VGG16, and VGG19, respectively. This study has proven the usefulness of deep learning models to detect COVID-19 in chest-CT images based on the proposed model framework. Therefore, it can contribute to the literature in Medical and Engineering in COVID-19 detection research.

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