An Ensemble Learning Approach for Brain Tumor Classification Using MRI

Kaur, Ranjeet; Doegar, Amit; Upadhyaya, Gaurav Kumar

doi:10.1007/978-981-16-1740-9_53

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…In [33], a CNN-based detection approach was introduced to identify abnormal brain MR images and reached 97.78% accuracy. In [34], the authors initiated an ensemble learning framework for classifying brain tumors using MR images and obtained 97.91% accuracy.…”

Section: Related Workmentioning

confidence: 99%

A New Distinctive Methodology for the Classification of Brain MR Images Using Histogram Based Local Feature Descriptors

Sowjanya,

Reddy,

Raveena

2023

IJCDS

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Brain tumors can develop at any brain location with uneven boundaries and shapes. Typically, they increased rapidly due to their size doubling in twenty-five days. If they were unrecognized in earlier phases, patients suffered from various medical problems, including death. Therefore, the identification of brain tumors in the earlier stages is one of the critical aspects. In addition, an effective imaging sequence also plays a vital role in tumor diagnosis. Magnetic resonance (MR) imaging is widely used among the available scanning approaches. Therefore, in this article, we develop a distinctive novel method to classify MR-based brain images. Here, initially, we improve the brightness of brain MR images using a median filter, and then we employ image data augmentation to increase the model's accuracy. Later, we obtain the region of interest (ROI) by Otsu's thresholding and morphological operations. Then, we extracted relevant local textures and shaped informative features from the ROI using Enhanced gradient local binary patterns (EGLBPs) and Modified pyramid histogram of oriented gradients (MPHOG). Finally, we perform classification using various supervised learning approaches: support vector machine (SVM), K-nearest neighbors (KNN), and ensemble learning. All these experiments are implemented on Harvard Medical School (HMS) database. Based on the simulation results, our proposed imaging system outperformed state-of-the-art methods in classification and segmentation. Hence, our suggested framework can be used as a predictive tool for diagnosing patients with brain tumors.

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Section: Related Workmentioning

confidence: 99%

A New Distinctive Methodology for the Classification of Brain MR Images Using Histogram Based Local Feature Descriptors

Sowjanya,

Reddy,

Raveena

2023

IJCDS

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Evaluate student achievement by classifying brain structure and its functionality with novel hybrid method

Ataş

2023

Neural Comput & Applic

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An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image

Suha

Islam

2022

Sci Rep

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Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality and one of the primary causes of anovulatory infertility in women globally. The detection of multiple cysts using ovary ultrasonograpgy (USG) scans is one of the most reliable approach for making an accurate diagnosis of PCOS and creating an appropriate treatment plan to heal the patients with this syndrome. Instead of depending on error-prone manual identification, an intelligent computer-aided cyst detection system can be a viable approach. Therefore, in this research, an extended machine learning classification technique for PCOS prediction has been proposed, trained and tested over 594 ovary USG images; where the Convolutional Neural Network (CNN) incorporating different state-of-the-art techniques and transfer learning has been employed for feature extraction from the images; and then stacking ensemble machine learning technique using conventional models as base learners and bagging or boosting ensemble model as meta-learner have been used on that reduced feature set to classify between PCOS and non-PCOS ovaries. The proposed technique significantly enhances the accuracy while also reducing training execution time comparing with the other existing ML based techniques. Again, following the proposed extended technique, the best performing results are obtained by incorporating the “VGGNet16” pre-trained model with CNN architecture as feature extractor and then stacking ensemble model with the meta-learner being “XGBoost” model as image classifier with an accuracy of 99.89% for classification.

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An Ensemble Learning Approach for Brain Tumor Classification Using MRI

Cited by 4 publications

References 15 publications

A New Distinctive Methodology for the Classification of Brain MR Images Using Histogram Based Local Feature Descriptors

A New Distinctive Methodology for the Classification of Brain MR Images Using Histogram Based Local Feature Descriptors

Evaluate student achievement by classifying brain structure and its functionality with novel hybrid method

An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image

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