Deep Transfer Learning Networks for Brain Tumor Detection: The Effect of MRI Patient Image Augmentation Methods

The exponential growth of deep learning networks has enabled us to handle difficult tasks, even in the complex field of medicine with small datasets. In the sphere of treatment, they are particularly significant. To identify brain tumors, this research examines how three deep learning networks are affected by conventional data augmentation methods, including MobileNetV2, VGG19, and DenseNet201. The findings showed that before and after utilizing approaches, picture augmentation schemes significantly affected the networks. The accuracy of MobileNetV2, which was originally 85.33%, was then enhanced to 96.88%. The accuracy of VGG19, which was 77.33%, was then enhanced to 95.31%, and DenseNet201, which was originally 82.66%, was then enhanced to 93.75%. The models' accuracy percentage engagement change is 13.53%, 23.25%, and 23.25%, respectively. Finally, the conclusion showed that applying data augmentation approaches improves performance, producing models far better than those trained from scratch

CHARACTERIZATION OF POLARIZATION-MAINTAINING FIBERS

In this thesis, first an introduction about Polarization maintaining fibers (PMF) and the alignment in PMF are discussed. Later, the polarization of light and methods for representing polarization state are reviewed along with some of their measurable properties and classification. More importantly the polarization-maintaining capability of a PANDA-style PM fiber is tested through a technique. The technique relies on changing angular position of the direction of the input linear polarized light and on recording the states of polarization at the output. Furthermore, measurements are taken when the PM fiber is bent. The measurements clearly depict the fact that when the input linearly polarized light direction is aligned to one of the two birefringent axes, the output polarization state is exactly the same as the input polarization state. Also the state of polarization remains unchanged even if the PM fiber is bent. However, if the necessary alignment is not considered, the input polarization state will be changed at the output completely. Also, bending the PM fiber makes the output polarization state even more variable