Gliomas are the most common primary brain tumors. They are classified into 4 grades (Grade I–II-III–IV) according to the guidelines of the World Health Organization (WHO). The accurate grading of gliomas has clinical significance for planning prognostic treatments, pre-diagnosis, monitoring and administration of chemotherapy. The purpose of this study is to develop a deep learning-based classification method using radiomic features of brain tumor glioma grades with deep neural network (DNN). The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool. This study primarily focuses on the four main aspects of the radiomic workflow, namely tumor segmentation, feature extraction, analysis, and classification. We evaluated data from 121 patients with brain tumors (Grade II, n = 77; Grade III, n = 44) from The Cancer Imaging Archive, and 744 radiomic features were obtained by applying low sub-band and high sub-band 3D wavelet transform filters to the 3D tumor images. Quantitative values were statistically analyzed with MannWhitney U tests and 126 radiomic features with significant statistical properties were selected in eight different wavelet filters. Classification performances of 3D wavelet transform filter groups were measured using accuracy, sensitivity, F1 score, and specificity values using the deep learning classifier model. The proposed model was highly effective in grading gliomas with 96.15% accuracy, 94.12% precision, 100% recall, 96.97% F1 score, and 98.75% Area under the ROC curve. As a result, deep learning and feature selection techniques with wavelet transform filters can be accurately applied using the proposed method in glioma grade classification.

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Brain tumors are a type of tumor with a high mortality rate. Since multifocal-looking tumors in the brain can resemble multicentric gliomas or gliomatosis, accurate detection of the tumor is required during the treatment process. The similarity of neurological and radiological findings also complicates the classification of these tumors. Fast and accurate classification is important for brain tumors. Computer aided diagnostic systems and deep neural network architectures can be used in the diagnosis of multicentric gliomas and multiple lesions. In this study, the Deep Neural Network classification model with Synthetic Minority Over-sampling Technique pre-processing was used on the Visually Accessible Rembrandt Images dataset. The proposed model for the classification of brain tumors consists of 1319 trainable parameters and the proposed method has achieved 95.0% accuracy rate. Precision, Recall, F1-measure values are 95.4%, 95.0% and 94.9% respectively. The proposed decision support system can be used to give an idea to doctors in the detection of glioma type tumors.

Diabetes mellitus is a common disease worldwide. In progressive diabetes patients, deterioration of kidney histology tissue begins. Currently, the histopathologic examination of kidney tissue samples has been performed manually by pathologists. This examination process is time-consuming and requires pathologists' expertise. Thus, automatic detection methods are crucial for early detection and also treatment planning. Computer-aided diagnostic systems based on deep learning show high success rates in classifying medical images if a large and diverse image set is available during the training process. Herein, transfer learningbased convolutional neural network model was proposed for the automatic detection of diabetes mellitus using only rat kidney histopathology images. The model monitors structural changes, especially in the glomerulus and also other parts of the kidney caused by the damages of diabetes. According to the simulation results, the proposed model has reached 97.5% accuracy. As a result, the recommended model can quickly and accurately classify histopathology images and helps pathologists as the second reader in critical situations

Glioma is one of the most common brain tumours among the diagnoses of existing brain tumours. Glioma grades are important factors that should be known in the treatment of brain tumours. In this study, the radiomic features of gliomas were analysed and glioma grades were classified by Gaussian Naive Bayes algorithm. Glioma tumours of 121 patients of Grade II and Grade III were examined. The glioma tumours were segmented with the Grow Cut Algorithm and the 3D feature of tumour magnetic resonance imaging images were obtained with the 3D Slicer programme. The obtained quantitative values were statistically analysed with Spearman and Mann–Whitney U tests and 21 features with statistically significant properties were selected from 107 features. The results showed that the best performing among the algorithms was Gaussian Naive Bayes algorithm with 80% accuracy. Machine learning and feature selection techniques can be used in the analysis of gliomas as well as pathological evaluations in glioma grading processes.   Keywords: Radiomics, glioma, naive bayes.

Breast cancer is cancer that forms in the cells of the breasts. Breast cancer is the most common cancer diagnosed in women in the world. Breast cancer can occur in both men and women, but it's far more common in women. Early detection of breast cancer tumours is crucial in the treatment. In this study, we presented a computer aided diagnosis expectation maximization segmentation and co-occurrence texture features from wavelet approximation tumour image of each slice and evaluated the performance of SVM Algorithm. We tested the model on 50 patients, among them, 25 are benign and 25 malign. The 80% of the images are allocated for training and 20% of images reserved for testing. The proposed model classified 2 patients correctly with success rate of 80% in case of 5 Fold Cross-Validation  Keywords: Breast Cancer, Computer-Aided Diagnosis (CAD), Magnetic Resonance Imaging (MRI);

Radiomics is an important quantitative feature extraction tool used in many areas such as image processing and computer-aided diagnosis. In this study, the discriminability of brain cancer tumour grades (Grade II and Grade III) with radiomic features were analysed statistically. The data set consists of 121 patients, 77 patients with Grade II tumours and 44 patients with Grade III tumours. A total of 107 radiomic features were extracted, including three groups of radiomic features such as morphological, first-order and texture. Relationships between the characteristics of each group were tested by Spearman’s correlation analysis. Differences between Grade II and Grade III tumour categories were analysed with Mann–Whitney U test. According to the results, it was seen that radiomic features can be used to differentiate the features of tumour levels evaluated in the same category. These results show that by employing radiomic features brain cancer grade detection can help machine learning technologies and radiological analysis.   Keywords: Radiomics, glioma, image processing.

Hazelnut is a product with high nutritional and economic value. In maintaining the quality value of hazelnut, the classification process is of great importance. In the present day, the quality classification of hazelnuts and other crops is performed in general manually, and so it is difficult and costly. Performing this classification with modern agricultural techniques is much more important in terms of quality. This study was based on a model intended to detect hazelnut quality. The model is about the establishment of an artificial intelligence-based classification system that can detect the hidden defects of hazelnuts. In the developed model, the visuals used in the dataset are divided into training and test groups. In the model, hazelnuts are divided into 5 classes according to their quality using AlexNet architecture and modern deep learning (DL) techniques instead of traditional hazelnut classification methods. In this model developed based on artificial intelligence, a very good approach was presented with the accurate classification of 99%. Moreover, the values regarding precision and recall were also determined at 98.7% and 99.6%, respectively. This study is important in terms of becoming widespread information technology use and computer-assisted applications in the agricultural economics field such as product classification, quality, and control.