Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Sci Rep. 2024 Nov 27;14(1):29448. doi: 10.1038/s41598-024-80888-z.
Brain tumours (BT) affect human health owing to their location. Artificial intelligence (AI) is intended to assist in diagnosing and treating complex diseases by combining technologies like deep learning (DL), big data analytics, and machine learning (ML). AI can identify and categorize tumours by analyzing brain imaging approaches like Magnetic Resonance Imaging (MRI). The medical sector has been promptly shifted by evolving technology, and an essential element of these transformations is AI technology. AI model determines tumours' class, size, aggressiveness, and location. This assists medical doctors in making more exact diagnoses and treatment plans and helps patients better understand their health. Also, AI is used to track the progress of patients through treatment. AI-based analytics is used to predict potential tumour recurrence and assess treatment response. This study presents Brain Tumor Recognition using an Equilibrium Optimizer with a Deep Learning Approach (BTR-EODLA) technique for MRI images. The BTR-EODLA technique intends to recognize whether or not a BT presence exists. In the BTR-EODLA technique, median filtering (MF) is deployed to eliminate the noise in the input MRI. Besides, the squeeze-excitation ResNet (SE-ResNet50) model is applied to derive feature vectors, and its parameters are fine-tuned by the design of the EO model. The BTR-EODLA technique utilizes the stacked autoencoder (SAE) model for BT detection. A sequence of experiments is performed to ensure the improved performance of the BTR-EODLA technique. The investigational validation of the BTR-EODLA technique portrayed a superior accuracy value of 98.78% over existing models.
脑肿瘤（BT）因其位置而影响人类健康。人工智能（AI）旨在通过结合深度学习（DL）、大数据分析和机器学习（ML）等技术，帮助诊断和治疗复杂疾病。人工智能可以通过分析磁共振成像（MRI）等脑成像方法来识别和分类肿瘤。不断发展的技术迅速改变了医疗行业，这些变革的一个重要因素是人工智能技术。AI 模型可以确定肿瘤的类别、大小、侵袭性和位置。这有助于医生做出更准确的诊断和治疗计划，帮助患者更好地了解自己的健康状况。此外，人工智能还用于跟踪患者的治疗进展。基于人工智能的分析用于预测潜在的肿瘤复发和评估治疗反应。本研究提出了一种基于平衡优化器和深度学习方法的磁共振成像脑肿瘤识别（BTR-EODLA）技术。BTR-EODLA 技术旨在识别是否存在 BT。在 BTR-EODLA 技术中，采用中值滤波（MF）消除输入 MRI 中的噪声。此外，应用挤压激励 ResNet（SE-ResNet50）模型提取特征向量，并通过 EO 模型的设计对其参数进行微调。BTR-EODLA 技术利用堆叠自动编码器（SAE）模型进行 BT 检测。进行了一系列实验以确保 BTR-EODLA 技术的改进性能。BTR-EODLA 技术的研究验证表明，其准确性值优于现有模型的 98.78%。
