Wong Yuki, Su Eileen Lee Ming, Yeong Che Fai, Holderbaum William, Yang Chenguang
Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
Manchester Metropolitan University, Manchester, United Kingdom.
PLoS One. 2025 May 9;20(5):e0322624. doi: 10.1371/journal.pone.0322624. eCollection 2025.
Brain tumors pose a significant medical challenge, necessitating early detection and precise classification for effective treatment. This study aims to address this challenge by introducing an automated brain tumor classification system that utilizes deep learning (DL) and Magnetic Resonance Imaging (MRI) images. The main purpose of this research is to develop a model that can accurately detect and classify different types of brain tumors, including glioma, meningioma, pituitary tumors, and normal brain scans. A convolutional neural network (CNN) architecture with pretrained VGG16 as the base model is employed, and diverse public datasets are utilized to ensure comprehensive representation. Data augmentation techniques are employed to enhance the training dataset, resulting in a total of 17,136 brain MRI images across the four classes. The accuracy of this model was 99.24%, a higher accuracy than other similar works, demonstrating its potential clinical utility. This higher accuracy was achieved mainly due to the utilization of a large and diverse dataset, the improvement of network configuration, the application of a fine-tuning strategy to adjust pretrained weights, and the implementation of data augmentation techniques in enhancing classification performance for brain tumor detection. In addition, a web application was developed by leveraging HTML and Dash components to enhance usability, allowing for easy image upload and tumor prediction. By harnessing artificial intelligence (AI), the developed system addresses the need to reduce human error and enhance diagnostic accuracy. The proposed approach provides an efficient and reliable solution for brain tumor classification, facilitating early diagnosis and enabling timely medical interventions. This work signifies a potential advancement in brain tumor classification, promising improved patient care and outcomes.
脑肿瘤构成了重大的医学挑战，需要早期检测和精确分类以进行有效治疗。本研究旨在通过引入一种利用深度学习（DL）和磁共振成像（MRI）图像的自动脑肿瘤分类系统来应对这一挑战。本研究的主要目的是开发一种能够准确检测和分类不同类型脑肿瘤的模型，包括胶质瘤、脑膜瘤、垂体瘤和正常脑部扫描。采用以预训练的VGG16为基础模型的卷积神经网络（CNN）架构，并利用各种公共数据集以确保全面代表性。采用数据增强技术来扩充训练数据集，最终得到涵盖四个类别的总共17136张脑MRI图像。该模型的准确率为99.24%，高于其他类似研究，证明了其潜在的临床实用性。如此高的准确率主要得益于使用了大量多样的数据集、改进了网络配置、应用了微调策略来调整预训练权重以及实施了数据增强技术以提高脑肿瘤检测的分类性能。此外，通过利用HTML和Dash组件开发了一个网络应用程序以提高可用性，允许轻松上传图像并进行肿瘤预测。通过利用人工智能（AI），所开发的系统满足了减少人为误差并提高诊断准确性的需求。所提出的方法为脑肿瘤分类提供了一种高效可靠的解决方案，有助于早期诊断并实现及时的医疗干预。这项工作标志着脑肿瘤分类方面的一项潜在进展，有望改善患者护理和治疗结果。
