Hybrid‐NET: A fusion of DenseNet169 and advanced machine learning classifiers for enhanced brain tumor diagnosis

Abstract

AbstractThe computer‐aided diagnostic (CAD) method to detect human brain tumors relies heavily on automated tumor characterization. Although CAD method has been extensively researched, significant obstacles still exist. Magnetic resonance imaging (MRI) classifiers brain tumors into glioma, meningioma, and pituitary tumors. However, accurately distinguishing between these tumor types remains a complex challenge in medical imaging. The recently developed deep learning and machine learning (ML) techniques have shown immense potential in image classification. However, the low numbers of medical image archives also pose a problem for medical image classification. As a result, fewer medical images are available to use in deep learning research and development. To address this issue, we use three highly effective ML classifiers with deep convolutional learned features for medical image classification. The MRI images of the three distinct types of brain tumors may be found in an open dataset on Figshare, which tests the automated approach's efficacy. Features are extracted from brain MRI scans using the DenseNet169 model. Extracted features are fed into a multiclass three ML classifiers (RF, SVM, XGBoost) for improved performance. Results from testing and evaluation of the entire framework are promising, especially compared to the field's state‐of‐the art technique. The suggested model outperformed the state‐of‐the‐art technique, with an overall classification accuracy of 95.10%. To verify the enhanced performance of the proposed system, extensive tests are conducted on the brain MRI dataset available on Figshare. The optimal hyper‐parameter fading classifier is seen to outperform the Softmax classifier for the features when there is limited training data.