ABSTRACT
Laryngeal cancer (LC) represents a substantial world health problem, with diminished survival rates attributed to late-stage diagnoses. Correct treatment for LC is complex, particularly in the final stages. This kind of cancer is a complex malignancy inside the head and neck region of patients. Recently, researchers serving medical consultants to recognize LC efficiently develop different analysis methods and tools. However, these existing tools and techniques have various problems regarding performance constraints, like lesser accuracy in detecting LC at the early stages, additional computational complexity, and colossal time utilization in patient screening. Deep learning (DL) approaches have been established that are effective in the recognition of LC. Therefore, this study develops an efficient LC Detection using the Chaotic Metaheuristics Integration with the DL (LCD-CMDL) technique. The LCD-CMDL technique mainly focuses on detecting and classifying LC utilizing throat region images. In the LCD-CMDL technique, the contrast enhancement process uses the CLAHE approach. For feature extraction, the LCD-CMDL technique applies the Squeeze-and-Excitation ResNet (SE-ResNet) model to learn the complex and intrinsic features from the image preprocessing. Moreover, the hyperparameter tuning of the SE-ResNet approach is performed using a chaotic adaptive sparrow search algorithm (CSSA). Finally, the extreme learning machine (ELM) model was applied to detect and classify the LC. The performance evaluation of the LCD-CMDL approach occurs utilizing a benchmark throat region image database. The experimental values implied the superior performance of the LCD-CMDL approach over recent state-of-the-art approaches.
ABSTRACT
Developing legally compliant systems is a challenging software engineering problem, especially in systems that are governed by law, such as healthcare information systems. This challenge comes from the ambiguities and domain-specific definitions that are found in governmental rules. Therefore, there is a significant business need to automatically analyze privacy texts, extract rules and subsequently enforce them throughout the supply chain. The existing works that analyze health regulations use the U.S. Health Insurance Portability and Accountability Act as a case study. In this article, we applied the Breaux and Antón approach to the text of the Saudi Arabian healthcare privacy regulations; in Saudi Arabia, privacy is among the top dilemmas for public and private healthcare practitioners. As a result, we extracted and analyzed 2 rights, 4 obligations, 22 constraints, and 6 rules. Our analysis can assist requirements engineers, standards organizations, compliance officers and stakeholders by ensuring that their systems conform to Saudi policy. In addition, this article discusses the threats to the study validity and suggests open problems for future research.
