Abstract
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.
Highlights
A brain tumor is a clump of irregular cells in the brain that forms a mass [1]
The Magnetic Resonance Imaging (MRI) picture of a healthy brain is shown in Figure 1 (A), while the picture of a brain containing a tumor is shown in Figure 1 (B)
The traditional algorithms were selected based on the literature review of the previous works and the selected algorithms include Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB) and K Nearest Neighbors (KNN)
Summary
A brain tumor is a clump of irregular cells in the brain that forms a mass [1]. The human brain is enclosed by a rigid skull. Any expansion in such a small area will trigger severe issues. Brain tumors can be cancerous and non-cancerous. The pressure within the skull will rise as benign or malignant tumors develop. This will result in permanent brain injury and even death. 700,000 people worldwide have a brain tumor, with approximately 86,000 new cases diagnosed in 2019. Since 2019, 16,830 people have died from brain tumors, with a 35 percent life expectancy [2]. Scientists and researchers have been working towards developing sophisticated techniques and methods for identifying brain
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