Lakshmi K, Amaran Sibi, Subbulakshmi G, Padmini S, Joshi Gyanenedra Prasad, Cho Woong
Department of Information Technology, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry, India.
Department of Computing Technologies, School of Computing, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, 603203, India.
Sci Rep. 2025 Jan 3;15(1):690. doi: 10.1038/s41598-024-84692-7.
Detecting brain tumours (BT) early improves treatment possibilities and increases patient survival rates. Magnetic resonance imaging (MRI) scanning offers more comprehensive information, such as better contrast and clarity, than any alternative scanning process. Manually separating BTs from several MRI images gathered in medical practice for cancer analysis is challenging and time-consuming. Tumours and MRI scans of the brain are exposed utilizing methods and machine learning technologies, simplifying the process for doctors. MRI images can sometimes appear normal even when a patient has a tumour or malignancy. Deep learning approaches have recently depended on deep convolutional neural networks to analyze medical images with promising outcomes. It supports saving lives faster and rectifying some medical errors. With this motivation, this article presents a new explainable artificial intelligence with semantic segmentation and Bayesian machine learning for brain tumors (XAISS-BMLBT) technique. The presented XAISS-BMLBT technique mainly concentrates on the semantic segmentation and classification of BT in MRI images. The presented XAISS-BMLBT approach initially involves bilateral filtering-based image pre-processing to eliminate the noise. Next, the XAISS-BMLBT technique performs the MEDU-Net+ segmentation process to define the impacted brain regions. For the feature extraction process, the ResNet50 model is utilized. Furthermore, the Bayesian regularized artificial neural network (BRANN) model is used to identify the presence of BTs. Finally, an improved radial movement optimization model is employed for the hyperparameter tuning of the BRANN technique. To highlight the improved performance of the XAISS-BMLBT technique, a series of simulations were accomplished by utilizing a benchmark database. The experimental validation of the XAISS-BMLBT technique portrayed a superior accuracy value of 97.75% over existing models.
早期检测脑肿瘤（BT）可改善治疗可能性并提高患者生存率。磁共振成像（MRI）扫描比任何其他扫描方法都能提供更全面的信息，例如更好的对比度和清晰度。在医学实践中收集的用于癌症分析的多个MRI图像中手动分离脑肿瘤具有挑战性且耗时。利用方法和机器学习技术来显示肿瘤和脑部MRI扫描，从而简化医生的操作流程。即使患者患有肿瘤或恶性肿瘤，MRI图像有时也可能看起来正常。深度学习方法最近依赖于深度卷积神经网络来分析医学图像，取得了有前景的成果。它有助于更快地挽救生命并纠正一些医疗错误。出于这种动机，本文提出了一种用于脑肿瘤的具有语义分割和贝叶斯机器学习的新型可解释人工智能（XAISS-BMLBT）技术。所提出的XAISS-BMLBT技术主要专注于MRI图像中脑肿瘤的语义分割和分类。所提出的XAISS-BMLBT方法首先涉及基于双边滤波的图像预处理以消除噪声。接下来，XAISS-BMLBT技术执行MEDU-Net+分割过程以定义受影响的脑区。对于特征提取过程，使用ResNet50模型。此外，贝叶斯正则化人工神经网络（BRANN）模型用于识别脑肿瘤的存在。最后，采用改进的径向运动优化模型对BRANN技术进行超参数调整。为了突出XAISS-BMLBT技术的改进性能，利用一个基准数据库完成了一系列模拟。XAISS-BMLBT技术的实验验证表明，其准确率比现有模型高出97.75%。
