Alazwari Sana, Maashi Mashael, Alsamri Jamal, Alamgeer Mohammad, Ebad Shouki A, Alotaibi Saud S, Obayya Marwa, Al Zanin Samah
Department of Information Technology, College of Computers and Information Technology, Taif University, Taif P.O. Box 11099, 21944 Taif, Saudi Arabia.
Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Po Box 103786, 11543 Riyadh, Saudi Arabia.
Health Inf Sci Syst. 2024 Jul 12;12(1):38. doi: 10.1007/s13755-024-00296-5. eCollection 2024 Dec.
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.
喉癌（LC）是一个严重的全球健康问题，晚期诊断导致生存率降低。LC的正确治疗很复杂，尤其是在晚期。这种癌症是患者头颈部区域的一种复杂恶性肿瘤。最近，为医学顾问服务以有效识别LC的研究人员开发了不同的分析方法和工具。然而，这些现有的工具和技术在性能限制方面存在各种问题，例如早期检测LC的准确性较低、额外的计算复杂性以及患者筛查中大量的时间消耗。已经建立了在识别LC方面有效的深度学习（DL）方法。因此，本研究开发了一种使用混沌元启发式算法与DL集成的高效LC检测（LCD-CMDL）技术。LCD-CMDL技术主要专注于利用喉部区域图像检测和分类LC。在LCD-CMDL技术中，对比度增强过程使用CLAHE方法。对于特征提取，LCD-CMDL技术应用挤压激励残差网络（SE-ResNet）模型从图像预处理中学习复杂的内在特征。此外，使用混沌自适应麻雀搜索算法（CSSA）对SE-ResNet方法进行超参数调整。最后，应用极限学习机（ELM）模型检测和分类LC。使用基准喉部区域图像数据库对LCD-CMDL方法进行性能评估。实验值表明LCD-CMDL方法的性能优于最近的最先进方法。
