School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, 11432, Saudi Arabia.
Sci Rep. 2022 Sep 12;12(1):15331. doi: 10.1038/s41598-022-19465-1.
The classification of brain tumors (BT) is significantly essential for the diagnosis of Brian cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on Computer aided diagnostic systems (CADS) are mostly used for the accurate detection of brain cancer. However, due to the inaccuracy of artificial diagnostic systems, medical professionals are not effectively incorporating them into the diagnosis process of Brain Cancer. In this research study, we proposed a robust brain tumor classification method using Deep Learning (DL) techniques to address the lack of accuracy issue in existing artificial diagnosis systems. In the design of the proposed approach, an improved convolution neural network (CNN) is used to classify brain tumors employing brain magnetic resonance (MR) image data. The model classification performance has improved by incorporating data augmentation and transfer learning methods. The results confirmed that the model obtained high accuracy compared to the baseline models. Based on high predictive results we suggest the proposed model for brain cancer diagnosis in IoT-healthcare systems.
脑肿瘤（BT）的分类对于物联网医疗系统中的脑癌（BC）诊断具有重要意义。基于人工智能（AI）技术的计算机辅助诊断系统（CADS）主要用于脑癌的精确检测。然而，由于人工诊断系统的不准确性，医疗专业人员并没有有效地将其纳入脑癌的诊断过程中。在这项研究中，我们提出了一种使用深度学习（DL）技术的强大脑肿瘤分类方法，以解决现有人工诊断系统中准确性不足的问题。在提出的方法的设计中，使用改进的卷积神经网络（CNN）来分类脑肿瘤，使用脑磁共振（MR）图像数据。通过合并数据增强和迁移学习方法，提高了模型的分类性能。结果证实，与基线模型相比，该模型获得了更高的准确性。基于高预测结果，我们建议在物联网医疗系统中使用该模型进行脑癌诊断。
