Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr, Sagunthala R&D Institute of Science and Technology, Chennai, India.
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India.
BMC Med Imaging. 2024 Nov 1;24(1):297. doi: 10.1186/s12880-024-01476-1.
Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental to the diagnosis because it contains detailed structural views of the brain, which is vital in identifying any of its abnormalities. The other option of performing an invasive biopsy is very painful and uncomfortable, which is not the case with MRI as it is free from surgically invasive margins and pieces of equipment. This helps patients to feel more at ease and hasten the diagnostic procedure, allowing physicians to formulate and practice action plans quicker. It is very difficult to locate a human brain tumor by manual because MRI scans produce large numbers of three-dimensional images. Complete applicability of pre-written computerized diagnostics, affords high possibilities in providing areas of interest earlier through the application of machine learning techniques and algorithms. The proposed work in the present study was to develop a deep learning model which will classify brain tumor grade images (BTGC), and hence enhance accuracy in diagnosing patients with different grades of brain tumors using MRI. A MobileNetV2 model, was used to extract the features from the images. This model increases the efficiency and generalizability of the model further. In this study, six standard Kaggle brain tumor MRI datasets were used to train and validate the developed and tested model of a brain tumor detection and classification algorithm into several types. This work consists of two key components: (i) brain tumor detection and (ii) classification of the tumor. The tumor classifications are conducted in both three classes (Meningioma, Pituitary, and glioma) and two classes (malignant, benign). The model has been reported to detect brain tumors with 99.85% accuracy, to distinguish benign and malignant tumors with 99.87% accuracy, and to type meningioma, pituitary, and glioma tumors with 99.38% accuracy. The results of this study indicate that the described technique is useful in the detection and classification of brain tumors.
早期发现脑肿瘤对于有效治疗和挽救生命至关重要。磁共振成像 (MRI) 扫描对大脑进行分析是诊断的基础，因为它包含了大脑的详细结构视图，这对于识别任何异常情况至关重要。另一种选择是进行有创活检，这种方法非常痛苦和不适，而 MRI 则没有手术侵入性边界和设备，这让患者感觉更舒适，并加速诊断过程，让医生更快地制定和实施行动计划。由于 MRI 扫描会产生大量的三维图像，因此手动定位人脑肿瘤非常困难。通过应用机器学习技术和算法，预先编写的计算机诊断的完全适用性为更早地提供感兴趣区域提供了很高的可能性。本研究提出的工作是开发一种深度学习模型，该模型将对脑肿瘤分级图像 (BTGC) 进行分类，从而提高使用 MRI 诊断不同级别脑肿瘤患者的准确性。使用 MobileNetV2 模型从图像中提取特征。该模型进一步提高了模型的效率和泛化能力。在这项研究中，使用了六个标准的 Kaggle 脑肿瘤 MRI 数据集来训练和验证开发的脑肿瘤检测和分类算法模型，并将其应用于多种类型的测试。这项工作包括两个关键部分：（i）脑肿瘤检测，（ii）肿瘤分类。肿瘤分类在三类（脑膜瘤、垂体瘤和神经胶质瘤）和两类（良性、恶性）中进行。该模型被报道可以以 99.85%的准确率检测脑肿瘤，以 99.87%的准确率区分良性和恶性肿瘤，以 99.38%的准确率对脑膜瘤、垂体瘤和神经胶质瘤进行分类。这项研究的结果表明，所描述的技术在脑肿瘤的检测和分类中是有用的。
