期刊
IEEE ACCESS
卷 9, 期 -, 页码 116942-116952出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3105874
关键词
Tumors; Magnetic resonance imaging; Brain modeling; Feature extraction; Convolutional neural networks; Deep learning; Machine learning; Brain tumor; deep learning; detection; machine learning; MRI; prediction
资金
- Military Institute of Science and Technology
A brain tumor is a life-threatening neurological condition caused by unregulated cell growth in the brain or skull, with early diagnosis being crucial for survival. Traditional methods for identifying brain tumors are time-consuming and rely heavily on expert knowledge, hence the importance of computer-assisted techniques.
A brain tumor is a life-threatening neurological condition caused by the unregulated development of cells inside the brain or skull. The death rate of people with this condition is steadily increasing. Early diagnosis of malignant tumors is critical for providing treatment to patients, and early discovery improves the patient's chances of survival. The patient's survival rate is usually very less if they are not adequately treated. If a brain tumor cannot be identified in an early stage, it can surely lead to death. Therefore, early diagnosis of brain tumors necessitates the use of an automated tool. The segmentation, diagnosis, and isolation of contaminated tumor areas from magnetic resonance (MR) images is a prime concern. However, it is a tedious and time-consuming process that radiologists or clinical specialists must undertake, and their performance is solely dependent on their expertise. To address these limitations, the use of computer-assisted techniques becomes critical. In this paper, different traditional and hybrid ML models were built and analyzed in detail to classify the brain tumor images without any human intervention. Along with these, 16 different transfer learning models were also analyzed to identify the best transfer learning model to classify brain tumors based on neural networks. Finally, using different state-of-the-art technologies, a stacked classifier was proposed which outperforms all the other developed models. The proposed VGG-SCNet's (VGG Stacked Classifier Network) precision, recall, and f1 scores were found to be 99.2%, 99.1%, and 99.2% respectively.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据