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    Breast Cancer Mass Classification Using Machine Learning, Binary-Coded Genetic Algorithms and an Ensemble of Deep Transfer Learning
    (Oxford Univ Press, 2023) Tiryaki, Volkan Mujdat; Tutkun, Nedim
    The diagnosis of breast cancer (BC) as early as possible is crucial for increasing the survival rate. Mammography enables finding the breast tissue changes years before they could develop into cancer symptoms. In this study, machine learning methods for BC mass pathology classification have been investigated using the radiologists' mass annotations on the screen-film mammograms of the Breast Cancer Digital Repository (BCDR). The performances of precomputed features in the BCDR and discrete wavelet transform followed by Radon transform have been investigated by using four sequential feature selections and three genetic algorithms. Feature fusion from craniocaudal and mediolateral oblique views was shown to increase the performance of the classifier. Mass classification has been implemented by deep transfer learning (DTL) using the weights of ResNet50, NASNetLarge and Xception networks. An ensemble of DTL (EDTL) was shown to have higher classification performance than the DTL models. The proposed EDTL has area under the receiver operating curve (AUC) scores of 0.8843 and 0.9089 for mass classification on the region of interest (ROI) and ROI union datasets, respectively. The proposed EDTL has the highest BC mass classification AUC score on the BCDR to date and may be useful for other datasets.
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    Öğe
    Sub-micro scale cell segmentation using deep learning
    (Wiley, 2022) Tiryaki, Volkan Mujdat; Ayres, Virginia M.; Ahmed, Ijaz; Shreiber, David, I
    Automated cell segmentation is key for rapid and accurate investigation of cell responses. As instrumentation resolving power increases, clear delineation of newly revealed cellular features at the submicron through nanoscale becomes important. Reliance on the manual investigation of myriad small features retards investigation; however, use of deep learning methods has great potential to reveal cell features both at high accuracy and high speed, which may lead to new discoveries in the near term. In this study, semantic cell segmentation systems were investigated by implementing fully convolutional neural networks called U-nets for the segmentation of astrocytes cultured on poly-l-lysine-functionalized planar glass. The network hyperparameters were determined by changing the number of network layers, loss functions, and input image modalities. Atomic force microscopy (AFM) images were selected for investigation as these are inherently nanoscale and are also dimensional. AFM height, deflection, and friction images were used as inputs separately and together, and the segmentation performances were investigated on five-fold cross-validation data. Transfer learning methods, including VGG16, VGG19, and Xception, were used to improve cell segmentation performance. We find that AFM height images inherit more discriminative features than AFM deflection and AFM friction images for cell segmentation. When transfer-learning methods are applied, statistically significant segmentation performance improvements are observed. Segmentation performance was compared to classical image processing algorithms and other algorithms in use by considering both AFM and electron microscopy segmentation. An accuracy of 0.9849, Matthews correlation coefficient of 0.9218, and Dice's similarity coefficient of 0.9306 were obtained on the AFM test images. Performance evaluations show that the proposed system can be successfully used for AFM cell segmentation with high precision.