Osteoporosis is a chronic bone disease affecting both men and women, but it is more prevalent in women. Promoting a healthy lifestyle among adults, particularly women, is crucial in preventing and reducing the osteoporosis impact. This study aimed to compare the osteoporosis knowledge, health beliefs and preventive behaviors among adult male and female in Najran city Saudi Arabia. This cross-sectional study was performed on 516 males and 581 females in Najran City, Saudi Arabia, from January to April 2023. The data collection instrument is a self-reported online questionnaire consisting of basic data, the Osteoporosis Preventive Behavior (OPB) scale, the osteoporosis knowledge assessment tool, and the osteoporosis health belief scale. The results revealed that male participants had a higher OPB (26.70) than females (20.32). However, females have a higher knowledge (10.71), perceive themselves as more susceptible to osteoporosis (20.34) and had higher exercise barriers (20.11) compared to males (9.97, 18.79 and 19.20, respectively). Statistically significant correlations (p < 0.001) were observed between OPB, osteoporosis knowledge (r = 0.26), perceived susceptibility (r = 0.33), severity (r = 0.53), exercise (r = 0.54) and calcium (r = 0.33) benefits, exercise (r = 0.40) and calcium (r = 0.81) barriers and health motivation (r = 0.37). The study concluded significant disparities between males and females regarding osteoporosis-related knowledge, preventive behaviors, perceived susceptibility, severity, and exercise perceived barriers. The results suggest gender-based educational interventions to enhance OPB by addressing osteoporosis-related knowledge, perceived susceptibility, seriousness, benefits, and health motivation.

Background: Lung cancer is a fatal disease that kills approximately 85% of those diagnosed with it. In recent years, advances in medical imaging have greatly improved the acquisition, storage, and visualization of various pathologies, making it a necessary component in medicine today. Objective: Develop a computer-aided diagnostic system to detect lung cancer early by segmenting tumor and non-tumor tissue on Tissue Micro Array Analysis (TMA) histopathological images. Method: The prototype computer-aided diagnostic system was developed to segment tumor areas, non-tumor areas, and fundus on TMA histopathological images. Results: The system achieved an average accuracy of 83.4% and an F-measurement of 84.4% in segmenting tumor and non-tumor tissue. Conclusion: The computer-aided diagnostic system provides a second diagnostic opinion to specialists, allowing for more precise diagnoses and more appropriate treatments for lung cancer.