A novel convolutional neural network‐based approach for brain tumor classification using magnetic resonance images

Cinar, Necip; Kaya, Mehmet; Kaya, Buket

doi:10.1002/ima.22839

Cited by 8 publications

(4 citation statements)

References 37 publications

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“…Cinar et al [ 34 ] applied image cropping and various data augmentation techniques, designing a CNN from scratch for brain tumour classification. They achieved overall accuracies of 98.09%, 98.32%, and 96.35% with different training and testing dataset divisions, demonstrating categorization without relying on deep networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier

Bhimavarapu,

Chintalapudi,

Battineni

2024

Bioengineering

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There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study’s commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.

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Section: Literature Reviewmentioning

confidence: 99%

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier

Bhimavarapu,

Chintalapudi,

Battineni

2024

Bioengineering

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“…In another work, authors Cinar et al. ( 29 ) presented a Convolutional Neural Network (CNN) architecture for brain tumor classification. The model was compared with ResNet50, VGG19, DensetNet121, and InceptionV3 pretrained models.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm

Ullah,

Khan,

Masood

et al. 2024

Front. Oncol.

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Brain tumor classification is one of the most difficult tasks for clinical diagnosis and treatment in medical image analysis. Any errors that occur throughout the brain tumor diagnosis process may result in a shorter human life span. Nevertheless, most currently used techniques ignore certain features that have particular significance and relevance to the classification problem in favor of extracting and choosing deep significance features. One important area of research is the deep learning-based categorization of brain tumors using brain magnetic resonance imaging (MRI). This paper proposes an automated deep learning model and an optimal information fusion framework for classifying brain tumor from MRI images. The dataset used in this work was imbalanced, a key challenge for training selected networks. This imbalance in the training dataset impacts the performance of deep learning models because it causes the classifier performance to become biased in favor of the majority class. We designed a sparse autoencoder network to generate new images that resolve the problem of imbalance. After that, two pretrained neural networks were modified and the hyperparameters were initialized using Bayesian optimization, which was later utilized for the training process. After that, deep features were extracted from the global average pooling layer. The extracted features contain few irrelevant information; therefore, we proposed an improved Quantum Theory-based Marine Predator Optimization algorithm (QTbMPA). The proposed QTbMPA selects both networks’ best features and finally fuses using a serial-based approach. The fused feature set is passed to neural network classifiers for the final classification. The proposed framework tested on an augmented Figshare dataset and an improved accuracy of 99.80%, a sensitivity rate of 99.83%, a false negative rate of 17%, and a precision rate of 99.83% is obtained. Comparison and ablation study show the improvement in the accuracy of this work.

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“…Cinar et al [48] applied image cropping initially to eliminate any unneeded areas in the image and make sure that the model focused only on those areas. Then, they implemented different data augmentation techniques on the original images: rotating, flipping, zooming, cropping, and translation.…”

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confidence: 99%

“…ACC. Accuracy, Sen. Ssensitivity, Spec.A robust MRI-based brain tumor classification via a hybrid…training-testing data split against[46][47][48]. Table4representsa comparison of our proposed technique with that of Deepak et al[50], who applied the majority technique and divided the same dataset into 60:20:20 for training, validation, and testing.…”

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confidence: 99%

A robust MRI-based brain tumor classification via a hybrid deep learning technique

Nassar¹,

Yasser²,

Amer³

et al. 2023

J Supercomput

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The brain is the most vital component of the neurological system. Therefore, brain tumor classification is a very challenging task in the field of medical image analysis. There has been a qualitative leap in the field of artificial intelligence, deep learning, and their medical imaging applications in the last decade. The importance of this remarkable development has emerged in the field of biomedical engineering due to the sensitivity and seriousness of the issues related to it. The use of deep learning in the field of detecting and classifying tumors in general and brain tumors in particular using magnetic resonance imaging (MRI) is a crucial factor in the accuracy and speed of diagnosis. This is due to its great ability to deal with huge amounts of data and avoid errors resulting from human intervention. The aim of this research is to develop an efficient automated approach for classifying brain tumors to assist radiologists instead of consuming time looking at several images for a precise diagnosis. The proposed approach is based on 3064 T1-weighted contrast-enhanced brain MR images (T1W-CE MRI) from 233 patients. In this study, the proposed system is based on the results of five different models to use the combined potential of multiple models, trying to achieve promising results. The proposed system has led to a significant improvement in the results, with an overall accuracy of 99.31%.

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A novel convolutional neural network‐based approach for brain tumor classification using magnetic resonance images

Cited by 8 publications

References 37 publications

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier

Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm

A robust MRI-based brain tumor classification via a hybrid deep learning technique

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