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Öğe A Convolutional Neural Networks Model for Breast Tissue Classification(2022) Biçer, Mustafa Berkan; Yanık, HüseyinThe diagnosis of breast cancer and the determination of the cancer types are essential pieces of information for cancer research in monitoring and managing the disease. In recent years, artificial intelligence techniques have led to many developments in medicine, as any information about the patient has become more valuable. In particular, artificial intelligence methods used in the detection and classification of cancer tissues directly assist physicians and contribute to the management of the treatment process. This study aims to classify breast tissues with ten different tissue characteristics by utilizing the breast tissue data set, which has 106 electrical impedance spectroscopies taken from 64 patients in the UCI Machine Learning Repository database. Various machine learning algorithms including k-nearest neighbors, support vector machine, decision tree, self-organizing fuzzy logic, and convolutional neural networks are used to classify these tissues with an accuracy of 81%, 78%, 82%, 92%, and 96%, respectively. This study demonstrated the benefit of the usage of convolutional neural networks in cancer detection and tissue classification. Compared to traditional methods, convolutional neural networks provided better and more reliable results.Öğe Radar-Based Microwave Breast Imaging Using Neurocomputational Models(MDPI, 2023) Biçer, Mustafa BerkanIn this study, neurocomputational models are proposed for the acquisition of radar-based microwave images of breast tumors using deep neural networks (DNNs) and convolutional neural networks (CNNs). The circular synthetic aperture radar (CSAR) technique for radar-based microwave imaging (MWI) was utilized to generate 1000 numerical simulations for randomly generated scenarios. The scenarios contain information such as the number, size, and location of tumors for each simulation. Then, a dataset of 1000 distinct simulations with complex values based on the scenarios was built. Consequently, a real-valued DNN (RV-DNN) with five hidden layers, a real-valued CNN (RV-CNN) with seven convolutional layers, and a real-valued combined model (RV-MWINet) consisting of CNN and U-Net sub-models were built and trained to generate the radar-based microwave images. While the proposed RV-DNN, RV-CNN, and RV-MWINet models are real-valued, the MWINet model is restructured with complex-valued layers (CV-MWINet), resulting in a total of four models. For the RV-DNN model, the training and test errors in terms of mean squared error (MSE) are found to be 103.400 and 96.395, respectively, whereas for the RV-CNN model, the training and test errors are obtained to be 45.283 and 153.818. Due to the fact that the RV-MWINet model is a combined U-Net model, the accuracy metric is analyzed. The proposed RV-MWINet model has training and testing accuracy of 0.9135 and 0.8635, whereas the CV-MWINet model has training and testing accuracy of 0.991 and 1.000, respectively. The peak signal-to-noise ratio (PSNR), universal quality index (UQI), and structural similarity index (SSIM) metrics were also evaluated for the images generated by the proposed neurocomputational models. The generated images demonstrate that the proposed neurocomputational models can be successfully utilized for radar-based microwave imaging, especially for breast imaging.
