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Classification of rice varieties with deep learning methods
Scopus
Toplam 222 atıf DOI
Rice, which is among the most widely produced grain products worldwide, has many genetic varieties. These varieties are separated from each other due to some of their features. These are usually features such as texture, shape, and color. With these features that distinguish rice varieties, it is possible to classify and evaluate the quality of seeds. In this study, Arborio, Basmati, Ipsala, Jasmine and Karacadag, which are five different varieties of rice often grown in Turkey, were used. A total of 75,000 grain images, 15,000 from each of these varieties, are included in the dataset. A second dataset with 106 features including 12 morphological, 4 shape and 90 color features obtained from these images was used. Models were created by using Artificial Neural Network (ANN) and Deep Neural Network (DNN) algorithms for the feature dataset and by using the Convolutional Neural Network (CNN) algorithm for the image dataset, and classification processes were performed. Statistical results of sensitivity, specificity, prediction, F1 score, accuracy, false positive rate and false negative rate were calculated using the confusion matrix values of the models and the results of each model were given in tables. Classification successes from the models were achieved as 99.87% for ANN, 99.95% for DNN and 100% for CNN. With the results, it is seen that the models used in the study in the classification of rice varieties can be applied successfully in this field.
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Identification of Corneal Ulcers with Pre- Trained AlexNet Based on Transfer Learning
Scopus
Havuzumuzda 9 atıf almış
Artificial intelligence methods are often used in the medical field because they give objective and consistent results. In this study, the SUSTech-SYSU data set was used for the automatic classification of corneal ulcers. Classification procedures were carried out using the pre-trained AlexNet model using fluorescein staining images of corneal ulcers, which were divided into 3 different classes and labeled (3 Categories, 5 Types, 5 Grades) in the dataset. Prior to the training of the pre-trained AlexNet model, data augmentation operations were performed on corneal ulcer images. The images labeled in different classes in the data set were evaluated separately for each class and classification operations were performed. As a result of the experiments, it was determined that the images with the Type label among the classes were the most effective class in detecting corneal ulcers, and as a result of the classification, an accuracy of 80.42% was achieved. After the Type-labeled image class, the Category and Grade classes, respectively, were included in the effectiveness ranking.
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Kurumlar (1)
Selçuk Üniversitesi
Selçuklu, Turkey