Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Department of Biology, College of Science and Arts at Alkamil, University of Jeddah, Jeddah, Saudi Arabia.
Sci Rep. 2024 Oct 25;14(1):25348. doi: 10.1038/s41598-024-75330-3.
Oral Squamous Cell Carcinoma (OSCC) causes a severe challenge in oncology due to the lack of diagnostic devices, leading to delays in detecting the disorder. The OSCC diagnosis through histopathology demands a pathologist expert because the cellular presentation is variable and highly complex. Existing diagnostic approaches for OSCC have specific efficiency and accuracy restrictions, highlighting the necessity for more reliable techniques. The increase of deep neural networks (DNN) model and their applications in medical imaging have been instrumental in disease diagnosis and detection. Automatic detection systems using deep learning (DL) approaches show tremendous promise in investigating medical imagery with speed, efficiency, and accuracy. In terms of OSCC, this system allows the diagnostic method to be streamlined, facilitating earlier diagnosis and enhancing survival rates. Automatic analysis of histopathological image (HI) can assist in accurately detecting and identifying tumorous tissue, reducing diagnostic turnaround times and increasing the efficacy of pathologists. This study presents a Squeeze-Excitation with Hybrid Deep Learning for Oral Squamous Cell Carcinoma Recognition (SEHDL-OSCCR) on HIs. The presented SEHDL-OSCCR technique mainly focuses on detecting oral cancer (OC) using hybrid DL models. The bilateral filtering (BF) technique is initially used to remove the noise. Next, the SEHDL-OSCCR technique employs the SE-CapsNet model to recognize the feature extractors. An improved crayfish optimization algorithm (ICOA) technique is utilized to improve the performance of the SE-CapsNet model. At last, the classification of the OSCC technique is performed by employing a convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) model. The simulation results obtained using the SEHDL-OSCCR technique are investigated using a benchmark medical image dataset. The experimental validation of the SEHDL-OSCCR technique illustrated a greater accuracy outcome of 98.75% compared to recent approaches.
口腔鳞状细胞癌 (OSCC) 由于缺乏诊断设备，在肿瘤学领域构成了严重挑战，导致疾病的检测出现延误。通过组织病理学进行 OSCC 诊断需要病理学家的专业知识，因为细胞表现具有多样性且非常复杂。现有的 OSCC 诊断方法具有特定的效率和准确性限制，这突显了对更可靠技术的需求。深度神经网络 (DNN) 模型的增加及其在医学成像中的应用在疾病诊断和检测方面发挥了重要作用。使用深度学习 (DL) 方法的自动检测系统在快速、高效和准确地研究医学图像方面具有巨大的潜力。就 OSCC 而言，该系统允许简化诊断方法，促进早期诊断并提高生存率。对组织病理学图像 (HI) 的自动分析有助于准确检测和识别肿瘤组织，减少诊断周转时间并提高病理学家的工作效率。本研究提出了一种基于挤压激励与混合深度学习的口腔鳞状细胞癌识别方法 (SEHDL-OSCCR) 用于 HI。所提出的 SEHDL-OSCCR 技术主要专注于使用混合 DL 模型检测口腔癌 (OC)。该技术首先使用双边滤波 (BF) 技术去除噪声。接下来，SEHDL-OSCCR 技术采用 SE-CapsNet 模型识别特征提取器。改进的小龙虾优化算法 (ICOA) 技术用于提高 SE-CapsNet 模型的性能。最后，通过使用具有双向长短期记忆 (CNN-BiLSTM) 模型的卷积神经网络对 OSCC 技术进行分类。使用 SEHDL-OSCCR 技术获得的模拟结果使用基准医学图像数据集进行了研究。与最近的方法相比，SEHDL-OSCCR 技术的实验验证表明其具有更高的准确性，达到了 98.75%。
