Abstract:
Accurate determination of the classification and severity of a cerebral neoplasm
is imperative in formulating a course of therapy and predicting the likely
outcome. Magnetic resonance imaging (MRI) has emerged as the preferred
imaging modality for the diagnosis of brain tumors, owing to its exceptional
sensitivity and specificity. The present study introduces a novel approach that
combines deep learning and machine learning techniques to classify brain tumors
from MRI images. The proposed model incorporates Convolutional Neural
Network (CNN) layers into the Inception v3 base model for the purpose
of feature extraction. The utilization of support vector machines (SVM) in
classification has resulted in an output layer of the model that attains a maximum
accuracy of 99.48% in the classification of a dataset comprising 5712
MRI images. The model’s performance was assessed using different evaluation
metrics such as sensitivity, specificity, and F1 score. The findings indicate
that the hybrid model suggested for the classification of brain tumors exhibits
superior performance compared to existing state-of-the-art techniques. The
clinical applicability potential of the model is underscored by its high precision
and robustness. The proposed hybrid model’s precision and effectiveness
in categorizing brain lesions from MRI images indicate the potential for the
advancement of machine learning algorithms in the field of medical image analysis.
The present study showcases the importance of deep learning and machine
learning models in augmenting the precision and effectiveness of brain tumor
diagnosis, thereby potentially influencing patient outcomes in a meaningful
manner.
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