Automatic Brain Tumor Classification in 2D MRI Images Using Integrated Deep Learning and Supervised Machine Learning Techniques

Precious, J. Glory; Kirubha, S. P. Angeline; Evangeline, I. Keren

doi:10.1007/978-981-16-7771-7_11

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…Precious et al [20] propose three optimizers, including ADAM, SGDM, and RMSprop, from whom detection accuracy of 98.1%, 92.5%, and 83.0% is attained. In order to detect tumors, four supervised machine learning classifiers are used once the features have been retrieved using CNN.…”

Section: Biswas Et Al In 2021mentioning

confidence: 99%

BrainNet-7: A CNN Model for Diagnosing Brain Tumors from MRI Images based on an Ablation Study

Rashid¹,

Akter²,

Akhi³

2022

IJACSA

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Tumors in the brain are masses or clusters of abnormal cells that may spread to other tissues nearby and pose a danger to the patient. The main imaging technique used to determine the extent of brain tumors is magnetic resonance imaging, which ensures an accurate diagnosis. A sizable amount of data for model training and advances in model designs that provide better approximations in a supervised environment likely account for most of the growth in Deep Learning techniques for computer vision applications. Deep learning approaches have shown promising results for increasing the precision of brain tumor identification and classification precision using magnetic resonance imaging (MRI). This study's purpose is to describe a robust deep-learning model that categorizes brain tumors using MRI images into four classes based on a convolutional neural network (CNN). By removing artefacts, reducing noise, and enhancing the image, unwanted areas of brain tumors are deleted, quality is improved, and the tumor is highlighted. Several CNN architectures, including VGG16, VGG19, MobileNet, MobileNetV2, and InceptionV3, are investigated to compare or get the best model. After getting the best model, a hyper parameter ablation study was performed on that model. Proposed BrainNet-7 achieved the best results with 99.01% test accuracy and 99.21% test and validation accuracy.

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Section: Biswas Et Al In 2021mentioning

confidence: 99%

BrainNet-7: A CNN Model for Diagnosing Brain Tumors from MRI Images based on an Ablation Study

Rashid¹,

Akter²,

Akhi³

2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…There is also a method in [17] for classifying MRI brain cancer based on grayscale, symmetry, and texture features. Three optimizers, namely ADAM, SGDM, and RMSprop, are suggested by Precious et al [18], from which detection rate of 98.1%, 92.5%, and 83.0% is acquired. To represent model experts, Papageorgiou et al [19] developed the fuzzy cognitive map (FCM).…”

Section: Introductionmentioning

confidence: 99%

A fine tune robust transfer learning based approach for brain tumor detection using VGG-16

Islam,

Akhi,

Akter

2023

Bulletin EEI

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Brain tumor recognition by magnetic resonance imaging (MRI) is crucial because it improves survival rates and allows them to plan treatments accordingly. An accumulation of abnormal cells known as a brain tumor can spread to nearby tissues and endanger the patient. Magnetic resonance imagery is the primary imaging technique which determines the extent of brain tumors. Deep learning techniques rapidly grew in computer vision due to ample data for model training and improved designs on applications. MRI has shown promising results when using deep learning approaches to identify and classify brain tumors. This study uses MRI data and a convolutional neural network (CNN) to create a reliable transfer learning model that classifies tumors under four classes. Brain tumors' unwanted parts are excised, the quality is improved, and the cancer is coloured. By eliminating artefacts, decreasing noise, and boosting the image. The number of MRI images has increased using two augmentation techniques. A number of CNN architectures, including VGG19, VGG16, MobileNet, InceptionV3, and MobileNetV2 analyzed the augmented dataset. Where VGG-16 provides the accuracy of highest level. The best model underwent a hyperparameter ablation investigation, which led to the suggested hyper-tuned VGG16 obtaining 99.21% test and validation accuracy and 99.01% test accuracy.

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Automatic Brain Tumor Classification in 2D MRI Images Using Integrated Deep Learning and Supervised Machine Learning Techniques

Cited by 2 publications

References 34 publications

BrainNet-7: A CNN Model for Diagnosing Brain Tumors from MRI Images based on an Ablation Study

BrainNet-7: A CNN Model for Diagnosing Brain Tumors from MRI Images based on an Ablation Study

A fine tune robust transfer learning based approach for brain tumor detection using VGG-16

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