LEE- Telekomünikasyon Mühendisliği Lisansüstü Programı
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Konu "breast cancer" ile LEE- Telekomünikasyon Mühendisliği Lisansüstü Programı'a göz atma
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ÖgeA novel antenna configuration for microwave hyperthermia(Graduate School, 2022-11-28) Altıntaş Yıldız, Gülşah ; Akduman, İbrahim ; Abdulsabeh Yılmaz, Tuğba ; 504182309 ; Telecommunications EngineeringBreast cancer affects approximately 2.5 million women each year and the consequences can be fatal. When treated correctly, however, the survival rates are very high. Surgical operation such as lumpectomy or mastectomy are invasive techniques that remove the partial or the whole breast. With early diagnosed cancers and the post-surgical patients, the most used therapy techniques are the radiotherapy, chemotherapy and the use of other anti-cancer agents. The economic and the psychological repercussions may be minimized by the increase efficiency of the treatments. It has been shown that with the artificial hyperthermia, elevated temperature levels at the cancer regions, the effectiveness of these modalities increase. Microwave breast hyperthermia (MH) aims to increase the temperature at the tumor location over its normal levels. During the procedure, the unwanted heated regions called hotspots can occur. The main aim of the MH is to prevent the hotspots while obtaining the necessary temperature at the tumor. Absorbed heat energy per kilogram at the breast, specific absorption rate (SAR), needs to be adjusted for a controlled MH. The choice of the MH applicator design is important for a superior energy focus on the target. Although hyperthermia treatment planning (HTP) changes for every patient, the MH applicator is required to be effective for different breast models and tumor types. In the first part of the thesis, the linear antenna arrays are implemented as MH applicators. We presented the focusing maps as an application guide for MH focusing by adjusting the antenna phase values. Furthermore, these focusing maps put forward the basic principle of focusing the energy at the breast. Sub-grouping the antenna, we obtained two phase main parameters that control the horizontal and vertical focus change. By adjusting these two phase values, we could focus the energy onto the target locations and we showed that with this simple structure, there is no need for optimization methods. However, the linear applicator performance was not successful for some target points, especially when the target is far away from both of the arrays. In the second part of the thesis, we improved the linear MH applicator. We concluded that the reason for the low performance of the linear applicator is mainly due to non-symmetrical geometry of the applicator and the resulting poor coverage. we proposed to radially re-adjust the position of the linear applicator for a better focusing ability while fixing the breast phantom. This generates multiple different applicator scheme without actually changing the applicator design. Particle swarm optimization (PSO) method is used for antenna excitation parameter selection. For the examined two targets, 135° rotated linear applicator gave 35-84% higher T BRS and 21-28% higher T BRT values than the fixed linear applicator, where T BRS stands for the target-to-breast SAR ratio and T BRT stands for the target-to-breast temperature ratio. Not only the rotated linear applicator gave higher performance, but also the circular array is rotated and the results were improved for one target. One of the main results of this study is that, for one target, the rotated linear applicator gave better results than the circular array, which is the state of the art. For the deep-seated target, 135° rotated linear applicator has 80% higher T BRS and 59% higher T BRT than the circular applicator with the same number of antennas. For the other target, the results of the linear and circular were comparable. However, the results obtained with the PSO were not robust. With different initial values (random in our study), the results were very different from each other, and we did 10 repetitions and took the best performing results. In the third part of the thesis, we presented deep-learning based antenna excitation parameter selection method. This method utilizes the learning ability of convolutional neural networks (CNN), rather than searching the solution space from random initial values as PSO does. The data set for CNN training was collected by superposing the electric fields obtained from individual antenna elements. We implemented a realistic breast phantom with and without a tumor inclusion. We used linear and circular applicators to validate the method. CNNs were trained offline with the data sets created first for the phase and then for the amplitude of the antennas. A mask of 1s and 0s is used to define the target region to be focused. This mask was given as the input to CNN models, and the corresponding phase and the amplitude values are calculated within seconds from the CNN models. The proposed approach outperforms the look-up table results, as the phase-only optimization and phase–power-combined optimization show a 27% and 4% lower hotspot-to-target energy ratio, respectively, than the look-up table results for the linear MH applicator
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ÖgeClinical assessment of the microwave imaging system forbreast cancer screening and early detection(Graduate School, 2023-04-26) Janjic, Aleksandar ; Çayören, Mehmet ; Akduman, İbrahim ; 504182310 ; Telecommunication EngineeringFemale breast cancer has surpased lung cancer, as the most diagnosed cancer in women population, with around 2.3 million cases arising each year. If diagnosed in late stages, it can be highly lethal, with the survival rate of only 25%. Thus, detecting the cancer in an early stage can have a major impact on decreasing the death rate of the patients. Nowadays, mammography is considered as a gold standard for breast cancer screening and diagnostics. Beside mammography, ultrasound, and magnetic resonance imaging can be used to detect the cancer. However, there are several risk factors that are limiting mentioned imaging modalities, such as: ionizing radiation exposure, pain induced by breast compression, overdiagnosis, false-positive examinations, falsenegativity in dense breasts, operator dependancy, prolonged procedures, high hospital costs, and special facility requirements. Microwave breast imaging emerged as a promising novel imaging technology that can, potentially, contribute to the field of breast cancer early screening and diagnostics, mostly because of its non-ionizing and non-invasive nature. Harmless radiation offers the opportunity of frequent scanning, even for the women of an early age, such as 18. Early-age and routine tests are crucial, especially for women with hereditary genetic mutations, where there is a considerable risk of breast cancer appearance. Beside its non-ionizing, and non-invasive nature, microwave imaging offers fast and painless scans, which can significantly increase the number of breast check-up tests, consequently increasing the number of detected early-stage cancers. Consequently, microwave breast imaging can have can substantially impact on the long-term breast cancer survival rate. The technology itself utilizes the difference in electromagnetic properties of healthy and cancerous tissue, as well as the dielectric difference between different type of cancerous tissues (benign or malignant), to detect the presence of anomalies inside the patient's breast and provide their pathology. In the first part of the thesis, we integrated inverse scattering algorithm to acquire the microwave images, and provide information about breast cancer location (detect the breast cancer), from the data collected with the microwave breast imaging device, namely SAFE, developed by the joint work of Mitos Medikal Technologies A.S. and the Medical Device Research, Development, and Application Laboratory of Istanbul Technical University. Dataset used in the study (scans from 115 patients), was acquired through the clinical trials performed by the Marmara University School of Medicine. In addition to the breast lesion detection, we analyzed the effect of the factors of interest, such as: breast density and size, tumor size, as well as patient's age, on the SAFE clinical capabilities. Results show, that we were able to detect 63% of breast lesions, where the breast size had a high impact on the overall score. Significantly lower number of lesions were detected in smaller breasts (51%), compared to the large ones (74%). Density also influenced our inverse scattering approach, as the overal rate of 76%, we achieved in fatty breasts, decreased to 56% in dense breasts. Second part of the thesis is reserved for the machine learning approach, namely adaptive boosting, we implemented on the SAFE dataset, to classify breast lesions, based on their pathology. We used the same dataset as in the first part of the thesis. As in the previous study, we analyze the effect of breast density and size, tumor size, and patient's age, on the used data. In addition, we perform statistical analysis (two-sample t-test) to determine if the difference between the benign and malignant dataset exists. In the existing dataset, 70 benign, and 43 malignant lesions were present. We exclude two cases, due to the unknown pathology. Our machine learning approach achieved the accuracy of 78%, sensitivity of 79% and specificity of 77%. The results indicate that we were able to classify both, benign and malignant lesions, at similar rate. Participant's age was the only factor that highly affected our approach outcome, where the overall rate (accuracy) of the device in young patient group was 84%, compared to the 76% achieved in older patient group. In the third part of the thesis, we implement another machine learning approach, namely Gradient Boosting, to distinguish benign from malignant lesions, considering new dataset, acquired from latest SAFE clinical trials. Additionally, compared to the previous studies, we changed the measurement unit component of the device. Fiftyfour patients were analyzed, where 29 of them had benign, and 25 malignat findings. As in the previous study, we apply statistical analysis (two-sample t-test), to determine if the difference between the benign and malignant dataset exists. Sensitivity, specificity and accuracy we achieved were 80%, 83% and 81%, showing that, in this study as well, we were able to classify both benign and malignant lesions at similar rate, despite of the hardware and software changes implemented. Contrary to the previous studies, multiple factors (breast size, density and age) affected our approach outcome. We achieved significantly higher accuracy in larger breasts (86%), compared to the smaller ones (78%). Additionally, accuracy acquired in dense breast (67%) was significantly lower than in fatty ones (93%). At the end, our method accuracy was 88% in older patient group, compared to the 71% in younger group.