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.
喉癌是一种严重疾病，其全球发病率呈上升趋势，令人担忧。作为一种复杂的头颈部恶性肿瘤，喉癌在晚期阶段的精准治疗尤为困难。为应对这一挑战，研究人员积极开发多种分析方法和工具，以协助医疗专业人员高效识别喉癌。然而，现有工具和方法常存在诸多局限性，包括早期喉癌检测准确率低、计算复杂度高以及患者筛查时间长等。基于此，本研究提出一种基于侏獴优化算法与深度学习的自动化喉癌检测与分类技术。该技术的主要目标是通过深度学习模型识别喉癌的存在。在所提出的方法中，首先采用基于中值滤波的噪声去除过程以消除噪声干扰。此外，该方法利用EfficientNet-B0模型从预处理图像中提取特征向量，并应用侏獴优化算法对EfficientNet-B0模型的超参数进行优化选择。最后，通过多头双向门控循环单元模型实现喉癌的识别与分类。该技术在喉部区域图像数据集上进行了仿真结果分析，对比研究表明其在多项评价指标上均表现出优越性能。
肿瘤(癌症)患者之家