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Öğe Detection of ring cell cancer in histopathological images with region of interest determined by SLIC superpixels method(Springer, 2022) Budak, Cafer; Mençik, VasfiyeGastric cancer is the sixth most common cancer and the fourth leading cause of cancer deaths worldwide. Gastric cancer presents with a more insidious onset and is most frequently discovered at an advanced stage. Early diagnosis is critical since the stage of the disease is determinant in the severity, treatment, and survival rate of cancer. In the study, the Region of Interest (RoI) was determined in histopathological images using image preprocessing techniques and signet ring cell carcinoma (SRCC) was detected with popular deep learning models VGG16, VGG19, and InceptionV3. The fine-tuning strategy was applied by customizing the last five layers of deep network models based on the target data. The parameters of accuracy, precision, recall, and F1-score were used to evaluate the model performance. Signet ring cell dataset taken from the competition "Digestive System Pathological Detection, and Segmentation Challenge 2019" was employed. When compared to results of the DigestPath2019 Grand challenge ring cell gastric cancer competition, higher accuracy rates were obtained using deep learning models with the accurate defined RoI images. VGG16 model exhibited a higher performance with accuracy of 95% and a F1-score of 95% among the models. The results obtained by the algorithm were analyzed and confirmed by the experienced pathologist.Öğe Determining similarities of COVID-19-lung cancer drugs and affinity binding mode analysis by graph neural network-based GEFA method(Taylor & Francis, 2021) Budak, Cafer; Mençik, Vasfiye; Gider, VeyselCOVID-19 is a worldwide health crisis seriously endangering the arsenal of antiviral and antibiotic drugs. It is urgent to find an effective antiviral drug against pandemic caused by the severe acute respiratory syndrome (Sars-Cov-2), which increases global health concerns. As it can be expensive and time-consuming to develop specific antiviral drugs, reuse of FDA-approved drugs that provide an opportunity to rapidly distribute effective therapeutics can allow to provide treatments with known preclinical, pharmacokinetic, pharmacodynamic and toxicity profiles that can quickly enter in clinical trials. In this study, using the structural information of molecules and proteins, a list of repurposed drug candidates was prepared again with the graph neural network-based GEFA model. The data set from the public databases DrugBank and PubChem were used for analysis. Using the Tanimoto/jaccard similarity analysis, a list of similar drugs was prepared by comparing the drugs used in the treatment of COVID-19 with the drugs used in the treatment of other diseases. The resultant drugs were compared with the drugs used in lung cancer and repurposed drugs were obtained again by calculating the binding strength between a drug and a target. The kinase inhibitors (erlotinib, lapatinib, vandetanib, pazopanib, cediranib, dasatinib, linifanib and tozasertib) obtained from the study can be used as an alternative for the treatment of COVID-19, as a combination of blocking agents (gefitinib, osimertinib, fedratinib, baricitinib, imatinib, sunitinib and ponatinib) such as ABL2, ABL1, EGFR, AAK1, FLT3 and JAK1, or antiviral therapies (ribavirin, ritonavir-lopinavir and remdesivir).Öğe Effect on model performance of regularization methods(Dicle Üniversitesi Mühendislik Fakültesi, 2021) Budak, Cafer; Mençik, Vasfiye; Asker, Mehmet EminArtificial Neural Networks with numerous parameters are tremendously powerful machine learning systems. Nonetheless, overfitting is a crucial problem in such networks. Maximizing the model accuracy and minimizing the amount of loss is significant in reducing in-class differences and maintaining sensitivity to these differences. In this study, the effects of overfitting for different model architectures with the Wine dataset were investigated by Dropout, AlfaDropout, GausianDropout, Batch normalization, Layer normalization, Activity normalization, L1 and L2 regularization methods and the change in loss function the combination with these methods. Combinations that performed well were examined on different datasets using the same model. The binary cross-entropy loss function was used as a performance measurement metric. According to the results, the Layer and Activity regularization combination showed better training and testing performance compared to other combinations.Öğe Online diagnosis of COVID-19 from chest radiography images by using deep learning algorithms(Springer Science and Business Media, 2023) Budak, Cafer; Mençik, Vasfiye; Varışlı, OsmanThe COVID-19 outbreak, which has a devastating impact on the health and well-being of the global population, is a respiratory disease. It is vital to determine, isolate and treat people with the disease as soon as possible to fight against the COVID-19 pandemic. Even though the reverse transcription polymerase chain reaction (RT-PCR) test, the accuracy of which is about 63%, seems to be a good option for determining COVID-19, it is a disadvantage is that test kits are few, are difficult to obtain in remote rural areas and have low accuracy. Chest X-ray (CXR) has become essential for rapidly diagnosing the rapidly spreading COVID-19 disease worldwide, so it is urgent to develop an online system that will help specialists identify infected patients with CXR images. In this study developed a transfer learning-based diagnosis system for online diagnosis of COVID-19 patients using CXR images. Transfer learning-based deep learning models VGG16, VGG19, ResNet50, InceptionV3, Xception, MobileNet, DenseNet121 and DenseNet201 were used for the experimental studies. We explored the COVID-19 radiography database from Kaggle, which is open to the public, using image preprocessing techniques and data augmentation. The images captured by the various terminals are transferred to the web server in the created system. Similar to the ensemble learning approach, the percentage accuracy of the model with the highest prediction value among the eight deep learning models is displayed on the screen. The results show that the proposed online diagnosis system performs better than others with the highest accuracy, precision, recall and F1 values of 98%, 99%, 97% and 97%, respectively. The results show that deep learning models help to increase the efficiency of chest radiograph scanning and have promising potential in predicting COVID-19 cases. The online diagnostic system will be a helpful tool for radiologists as it diagnoses COVID-19 quickly and with high accuracy.
