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    Automated Classification of Brain Tumor Disease with a Novel CNN Relief and SVM-Based Deep Hybrid Model
    (Int Information & Engineering Technology Assoc, 2023) Bayram, Hande Yuksel; Bingol, Harun; Alatas, Bilal
    The brain tumor is a very dangerous type of cancer that can be seen in people of almost any age and usually results in the patient's death. Early detection of these tumors, which have many varieties, is extremely important in terms of the patient's survival, affecting the planning of treatment, just as with other types of cancer. Early diagnosis of the disease is usually performed by means of imaging devices. It takes a lot of expertise to analyze the MRI images and diagnose the brain tumor. In this study, a hybrid deep model is recommended that can be used effectively in the classification of the brain tumor. The proposed hybrid model is a Convolutional Neural Network (CNN)-based method that automatically classifies Magnetic Resonance (MR) images of three different types of brain tumors, Glioma, Meningioma and Pituitary successfully. Our model is basically going through these stages. First of all, the features from the two models that show the highest performance from pre-trained deep models are combined. The most effective features of the specification map obtained in the next phase were selected using the Relief method. At the last stage, classification was carried out with Support Vector Machine (SVM), one of the most known machine learning techniques. As a result of the experiments, the hybrid deep model we proposed obtained 93.2% accuracy. It seems that proposed hybrid method has very competitive results and is thought to be efficiently used to classify the brain tumor.
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    Hybrid Deep Model for Automated Detection of Tomato Leaf Diseases
    (Int Information & Engineering Technology Assoc, 2022) Bayram, Hande Yuksel; Bingol, Harun; Alatas, Bilal
    Tomatoes are preferred by farmers because of their high productivity. This fruit has a fibrous structure and contains plenty of vitamins. Tomato diseases are generally observed on stem, fruit, and leaves. Early diagnosis of the disease in plants is of vital importance for the plant. This is very important for farmers who expect economic gain from that plant. Because if the disease is not treated early, these tomatoes should be destroyed. For these reasons, systems to diagnose the disease early are very important. In this study, a tomato leaf diseases classification model developed with deep learning methods, which is one of the most popular artificial intelligence techniques, is proposed in order to eliminate the possibility of the human eye being mistaken. In this study, 6 different Convolutional Neural Network (CNN) architectures were used. In the first stage of this study, which consists of two stages, the classification process was carried out with the Alexnet, Googlenet, Shufflenet, Efficientb0, Resnet50, and Inceptionv3 architectures that were previously trained. In the second stage, feature maps of tomato leaf images in the dataset were obtained using the six pre-trained deep learning architectures. In the hybrid model proposed in this study, the feature maps extracted using the best two of the six deep learning models are concatenated. Then, the Neighborhood Component Analysis (NCA) method was applied to the extracted features in order to speed up the system, unnecessary features were removed and optimized. The optimized feature map is classified by traditional intelligent classification models. As a result of experimental studies, the average accuracy rate of the proposed model is 99.50 percent.