Predictive Modeling of Brain Tumor: A Deep Learning Approach

Saxena, Priyansh; Maheshwari, Akshat; Maheshwari, Saumil

doi:10.1007/978-981-15-6067-5_30

Cited by 90 publications

(36 citation statements)

References 8 publications

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“…Inception V3 was the least successful model of this study with 68.13% accuracy. In the literature, results obtained in other studies using the Inception V3 model in the classification of brain MR images were also between 55% and 69% (Saxena et al 2020) (Zhou et al 2019).…”

Section: Figure 11 Accuracy and Loss Graphs Of The Inception V3 Modelmentioning

confidence: 93%

Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images

Sevli¹

2021

acin

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Section: Figure 11 Accuracy and Loss Graphs Of The Inception V3 Modelmentioning

confidence: 93%

Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images

Sevli¹

2021

acin

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“…The 95% accuracy of Ref. [23] achieved by one of the three proposed CNNs, is based on ResNet50 architecture; although with obvious overfitting problems, as Saxena et al state. Another successful attempt of classifying images with and without brain tumors is displayed by Sajja and Kalluri [24], where the usage of a CNN on a BRATS dataset containing 577 images, gives an accuracy of 96.15%, tested on 182 images.…”

Section: Related Workmentioning

confidence: 94%

Brain Tumor Detection Based on Features Extracted and Classified Using a Low-Complexity Neural Network

Papageorgiou¹

2021

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Brain tumor detection or brain tumor classification is one of the most challenging problems in modern medicine, where patients suffering from benign or malignant brain tumors are usually characterized by low life expectancy making the necessity of a punctual and accurate diagnosis mandatory. However, even today, this kind of diagnosis is based on manual classification of magnetic resonance imaging (MRI), culminating in inaccurate conclusions especially when they derive from inexperienced doctors. Hence, trusted, automatic classification schemes are essential for the reduction of humans’ death rate due to this major chronic disease. In this article, we propose an automatic classification tool, using a computationally economic convolutional neural network (CNN), for the purposes of a binary problem concerning MRI images depicting the existence or the absence of brain tumors. The proposed model is based on a dataset containing real MRI images of both classes with nearly perfect validation-testing accuracy and low computational complexity, resulting a very fast and reliable training-validation process. During our analysis we compare the diagnostic capacity of three alternative loss functions, validating the appropriateness of cross entropy function, while underlining the capability of an alternative loss function named Jensen-Shannon divergence since our model accomplished nearly excellent testing accuracy, as with cross-entropy. The multiple validation tests applied, enhancing the robustness of the produced results, render this low-complexity CNN structure as an ideal and trustworthy medical aid for the classification of small datasets.

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“…Despite the valuable works being done in this area, developing a robust and practical method still requires more effort to classify brain MR images. Saxena et al [ 32 ] used Inception V3, ResNet-50, and VGG-16 models with transfer learning methods to classify brain tumor data. The ResNet-50 model obtained the highest accuracy rate with 95%.…”

Section: Related Workmentioning

confidence: 99%

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Kang

Ullah

Gwak

2021

Sensors

351

140

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Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.

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Predictive Modeling of Brain Tumor: A Deep Learning Approach

Cited by 90 publications

References 8 publications

Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images

Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images

Brain Tumor Detection Based on Features Extracted and Classified Using a Low-Complexity Neural Network

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

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