Mohamed Nuzaiha, Almutairi Reem Lafi, Abdelrahim Sayda, Alharbi Randa, Alhomayani Fahad Mohammed, Elamin Elnaim Bushra M, Elhag Azhari A, Dhakal Rajendra
Department of Public Health, College of Public Health and Health Informatics, University of Hail, Ha'il 81451, Saudi Arabia.
Department of Statistics, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia.
Cancers (Basel). 2023 Dec 29;16(1):181. doi: 10.3390/cancers16010181.
Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, researchers have been actively developing various analysis methods and tools to assist medical professionals in efficient LCA identification. However, existing tools and methods often suffer from various limitations, including low accuracy in early-stage LCA detection, high computational complexity, and lengthy patient screening times. With this motivation, this study presents an Automated Laryngeal Cancer Detection and Classification using a Dwarf Mongoose Optimization Algorithm with Deep Learning (ALCAD-DMODL) technique. The main objective of the ALCAD-DMODL method is to recognize the existence of LCA using the DL model. In the presented ALCAD-DMODL technique, a median filtering (MF)-based noise removal process takes place to get rid of the noise. Additionally, the ALCAD-DMODL technique involves the EfficientNet-B0 model for deriving feature vectors from the pre-processed images. For optimal hyperparameter tuning of the EfficientNet-B0 model, the DMO algorithm can be applied to select the parameters. Finally, the multi-head bidirectional gated recurrent unit (MBGRU) model is applied for the recognition and classification of LCA. The simulation result analysis of the ALCAD-DMODL technique is carried out on the throat region image dataset. The comparison study stated the supremacy of the ALCAD-DMODL technique in terms of distinct measures.
喉癌（LCA）是一种严重的疾病，其全球发病率呈令人担忧的上升趋势。由于LCA作为头颈部恶性肿瘤的复杂性，在晚期对其进行准确治疗尤其具有挑战性。为应对这一挑战，研究人员一直在积极开发各种分析方法和工具，以协助医疗专业人员高效识别LCA。然而，现有的工具和方法往往存在各种局限性，包括早期LCA检测准确率低、计算复杂度高以及患者筛查时间长。出于这个动机，本研究提出了一种使用深度学习的侏儒猫鼬优化算法进行自动喉癌检测与分类（ALCAD-DMODL）技术。ALCAD-DMODL方法的主要目标是使用深度学习模型识别LCA的存在。在所提出的ALCAD-DMODL技术中，进行了基于中值滤波（MF）的去噪过程以消除噪声。此外，ALCAD-DMODL技术涉及使用EfficientNet-B0模型从预处理图像中提取特征向量。为了对EfficientNet-B0模型进行最佳超参数调整，可以应用侏儒猫鼬优化算法来选择参数。最后，应用多头双向门控循环单元（MBGRU）模型对LCA进行识别和分类。在喉部区域图像数据集上对ALCAD-DMODL技术进行了仿真结果分析。比较研究表明了ALCAD-DMODL技术在不同指标方面的优越性。
