Efficient brain tumor grade classification using ensemble deep learning models.

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Sankar M, Baiju Bv, Preethi D, Ananda Kumar S, Sandeep Kumar Mathivanan, Mohd Asif Shah

Author Information

Sankar M: Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr, Sagunthala R&D Institute of Science and Technology, Chennai, India.
Baiju Bv: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India.
Preethi D: Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, India.
Ananda Kumar S: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India.
Sandeep Kumar Mathivanan: School of Computer Science and Engineering, Galgotias University, Greater Noida, 203201, India.
Mohd Asif Shah: Department of Economics, Kardan University, Parwane Du, Kabul, 1001, Afghanistan. m.asif@kardan.edu.af.

PMID: 39487431 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.

Brain tumor Brain tumor grade classification Computerized diagnostics Machine learning Magnetic resonance image

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Humans

Deep Learning

Brain Neoplasms

Magnetic Resonance Imaging

Neoplasm Grading

Algorithms

Image Interpretation, Computer-Assisted

Journal Article

Enhancing brain tumor MRI classification with an ensemble of deep learning models and transformer integration.Ensemble deep learning for brain tumor detection.MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers.Brain Tumor Classification Using Deep Neural Network and Transfer Learning.Brain tumor classification using deep CNN features via transfer learning.Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models.Detection and classification of brain tumor using hybrid deep learning models.T1-weighted MRI-based brain tumor classification using hybrid deep learning models.Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods.Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data.

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