The Magnitude of Salt Intake Behaviors and Its Predictors among Saqez Urban Population of Kurdistan District in Iran: A Cross-Sectional Study.

Background: Controlling and reducing salt intake are one of the solutions to overcome hypertension. This study aimed at determining the predictors related to salt control methods in Saqez urban population.

Methods And Materials: In the present cross-sectional study, the sample population was randomly selected through cluster sampling.

A whole-slide image grading benchmark and tissue classification for cervical cancer precursor lesions with inter-observer variability.

The cervical cancer developing from the precancerous lesions caused by the human papillomavirus (HPV) has been one of the preventable cancers with the help of periodic screening. Cervical intraepithelial neoplasia (CIN) and squamous intraepithelial lesion (SIL) are two types of grading conventions widely accepted by pathologists. On the other hand, inter-observer variability is an important issue for final diagnosis.

A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods.

Sperm morphology, as an indicator of fertility, is a critical tool in semen analysis. In this study, a smartphone-based hybrid system that fully automates the sperm morphological analysis is introduced with the aim of eliminating unwanted human factors. Proposed hybrid system consists of two progressive steps: automatic segmentation of possible sperm shapes and classification of normal/ab-normal sperms.

Automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms.

The analysis of cell characteristics from high-resolution digital histopathological images is the standard clinical practice for the diagnosis and prognosis of cancer. Yet, it is a rather exhausting process for pathologists to examine the cellular structures manually in this way. Automating this tedious and time-consuming process is an emerging topic of the histopathological image-processing studies in the literature.

Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.

In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images.