Fourier ptychographic and deep learning using breast cancer histopathological image classification.

Automated, as well as accurate classification with breast cancer histological images, was crucial for medical applications because of detecting malignant tumors via histopathological images. In this work create a Fourier ptychographic (FP) and deep learning using breast cancer histopathological image classification. Here the FP method used in the process begins with such a random guess that builds a high-resolution complex hologram, subsequently uses iterative retrieval using FP constraints to stitch around each other low-resolution multi-view means of production owned from either the hologram's high-resolution hologram's elemental images captured via integral imaging.

Digital holographic technique based breast cancer detection using transfer learning method.

The digital holographic technique is an interferometric method that provides comprehensive information on morphological traits such as cell layer thickness and shape as well as access to biophysical attributes of cells like refractive index, dry mass, and volume. This method helps characterize sample structures in three dimensions both statically and dynamically, even for transparent objects like living biological cells. This research work captures the digital holograms of breast tissues and analyzes the malignancy of the tissue using a deep learning technique.

Biocompatible and Antibacterial SnO2 Nanowire Films Synthesized by E-Beam Evaporation Method.

In this work, the biocompatibility and antibacterial activities of novel SnO2 nanowire coatings prepared by electron-beam (E-Beam) evaporation process at low temperatures were studied. The nanowire coatings were characterized by scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX), and X-ray diffraction (XRD) methods. The results of in vitro cytotoxicity and cell proliferation assays suggested that the SnO2 nanowire coatings were nontoxic and promoted the proliferation of C2C12 and L929 cells (> 90% viability).