Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features

Emiroglu, Bulent Gursel/0000-0002-1656-6450; Cinarer, Gokalp/0000-0003-0818-6746; YURTTAKAL, Ahmet Hasim/0000-0001-5170-6466WOS:000580451100001Gliomas 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

Detection of breast cancer via deep convolution neural networks using MRI images

Ikizceli, Turkan/0000-0002-5683-0391; Erbay, Hasan/0000-0002-7555-541X; YURTTAKAL, Ahmet Hasim/0000-0001-5170-6466WOS:000538675900065Breast cancer is the type of cancer that develops from cells in the breast tissue. It is the leading cancer in women. Early detection of the breast cancer tumor is crucial in the treatment process. Mammography is a valuable tool for identifying breast cancer in the early phase before physical symptoms develop. To reduce false-negative diagnosis in mammography, a biopsy is recommended for lesions with greater than a 2% chance of having suspected malignant tumors and, among them, less than 30 percent are found to have malignancy. To decrease unnecessary biopsies, recently, Magnetic Resonance Imaging (MRI) has also been used to diagnose breast cancer. MRI is the highly recommended test for detecting and monitoring breast cancer tumors and interpreting lesioned regions since it has an excellent capability for soft tissue imaging. However, it requires an experienced radiologist and time-consuming process. On the other hand, convolutional neural networks (CNNs) have demonstrated better performance in image classification compared to feature-based methods and show promising performance in medical imaging. Herein, CNN was employed to characterize lesions as malignant or benign tumors using MRI images. Using only pixel information, a multi-layer CNN architecture with online data augmentation was designed. Later, the CNN architecture was trained and tested. The accuracy of the network is 98.33% and the error rate 0.0167. The sensitivity of the network is 1.0 whereas specificity is 0.9688. The precision is 0.9655

Solar irradiation forecastby deep learning architectures

Global solar irradiation data is a crucial component to measure solar energy potential when we plan, size, and design solar photovoltaic fields. Often, due to the absence of measuring equipment at meteorological stations, data for the place of interest are not available. However, solar irradiation can be estimated by ordinary meteorological data such as humidity, and air temperature. Herein we propose two different deep learning methods, one based on a deep neural network regression and the other based on multivariate long short term memory unit networks, to estimate solar irradiation at given locations. Validation criteria include mean absolute error, mean squared error, and coefficient of determination (R2 value). According to the simulation results, multivariate long short term memory unit networks performs slightly better than deep neural network. Even though both have very close R2 values, multivariate long short term memory’s R2 values are more consistent. The same is true for mean squared error and mean absolute error

A COMPARATIVE STUDY ON SEGMENTATION AND CLASSIFICATION IN BREAST MRI IMAGING

Cinarer, Gokalp/0000-0003-0818-6746; Erbay, Hasan/0000-0002-7555-541XWOS: 000455271800005Background: Breast cancer is the type of cancer that develops from cells in the breast tissue. The breast cancer is leading cancer in women. One in every eight to nine women has breast cancer at some point during their lifetime. Computer-Aided Diagnosis (CAD) Technology is getting more important to assist radiologists not only to detect breast cancer tumor but also to interpret lesioned regions. The CAD, as a second reader in the clinic, improves the classification of malignant and benign lesions. On the other hand, Magnetic Resonance Imaging (MRI) is a highly recommended test for detecting and monitoring breast cancer tumors and interpreting lesioned regions since it has an excellent capability for soft tissue imaging. In MRI image analysis, the segmentation images are important objective because accurate measurement of the delineation of the regions of interest (ROI) is critical for the breast cancer diagnosis and treatment. Herein, by using MRI scans, we propose a semi-automated CAD system prototype to assist radiologists in detecting breast cancer tumors and interpreting lesioned regions. The prototype, first, pre-processes the raw selected suspicious region to reduce the noises and to reveal the structure. Later, using Expectation Maximization (EM), the prototype segments the pre-processed region. After that, we use the Discrete Wavelet Transform (DWT) for providing efficient multi-resolution sub and decomposition of signals. Then Random Forest Algorithm is used for feature selection. Finally, Naive Bayes, Linear Discriminant Analysis and C4.5 Decision Tree Algorithms are used to classify the features of the ROI in the diagnosis analysis. We tested the prototype CAD on 105 patients, among them, 53 are benign and 52 malign. 80% of the images are allocated for training and 20% of images reserved for testing. The CAD classified 20 patients correctly in case of 5 fold cross-validation. Only one patient is misclassified. The computer-aided diagnosis system with the C4.5 has accuracy 95.24%. Furthermore, C4.5 classifies the breast cancer tumors better than Naive Bayes and Linear Discriminant Analysis. We tested the prototype CAD on 105 patients, among them, 53 are benign and 52 malign. The computer-aided diagnosis system with the C4.5 has accuracy 95.24%. Furthermore, C4.5 classifies the breast cancer tumors better than Naive Bayes and Linear Discriminant Analysis