Ensemble of Deep Learning Models for Brain Tumor Detection

Patil, Suraj; Kirange, D. K.

doi:10.1016/j.procs.2023.01.222

Cited by 30 publications

(18 citation statements)

References 13 publications

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“… 23 Tandel and Patil conducted studies on transfer-learning-based AI systems and CNN respectively, achieving significant results in multi-class brain tumour grading with accuracy rates of 94.7% and 97.77% respectively ( Table 1 ). 24 , 25 Alnowami et al also showed promising results in brain tumour classification using MRI, achieving an accuracy rate of 96.25%, sensitivity rate of 98.5% and specificity rate of 82.1% ( Table 1 ). 26 These studies demonstrate that AI can potentially improve patient outcomes by providing quick diagnosis and accurate tumour classification in neurosurgical oncology.…”

Section: Application and Outcomes Of Ai ML And Dl In Various Neurosur...mentioning

confidence: 96%

Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery

Awuah,

Adebusoye,

Wellington

et al. 2024

World Neurosurgery: X

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Section: Application and Outcomes Of Ai ML And Dl In Various Neurosur...mentioning

confidence: 96%

Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery

Awuah,

Adebusoye,

Wellington

et al. 2024

World Neurosurgery: X

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“…The VGG16 method, as proposed by Patil et al [10], was used to detect the brain tumor. Subsequently, an analysis was conducted to evaluate the measures of loss and accuracy utilizing the T1C MRI.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed model can achieve a computational time of 13 minutes. [10] 91 seconds per epoch CNN [12] 99 seconds per epoch 22 layer-CNN [20] 13 minutes CNN [22] 21 minutes and 66 seconds CNN [23] 27 minutes Proposed Model 13 minutes We examined all of the RFE-based features used to detect brain tumors, as well as individual features taken directly from the EfficientNet-CNN framework, to determine the importance of selected RFE features. The primary goal of this study is to analyze the various features for classifying MRI data using supervised SVM.…”

Section: Confusion Matrixmentioning

confidence: 99%

Brain tumor detection through novel feature selection using deep efficientNet-CNN-based features with supervised learning and data augmentation

Mujahid,

Rehman,

Alamri

et al. 2024

Phys. Scr.

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Brain tumors being ninth in terms of prevalence and one of the most frequently diagnosed malignant tumors, negatively impact millions of individuals. Identifying and classifying tumors from MRI used for health monitoring poses a challenge for radiologists, yet early detection could significantly enhance the chances of effective treatment. Researchers in the field of explainable AI are currently focused on developing sophisticated techniques to classify and diagnose brain diseases. This study presents a novel framework that enhances the interpretability of our proposed system for brain tumor detection by utilizing explainable AI techniques. To enhance interpretability, we integrate the optimized recursive feature elimination selection technique with support vector machines. This method effectively eliminates redundant features, identifies the most important ones, and enhances the efficiency of detecting tasks. Following that, the optimal recursive feature elimination (ORFE) features are combined using the supervised support vector machine (SVM) technique. While EfficientNet-CNN is a very useful feature extraction framework that extracts the most important features from a transparent model, we reduced the overall computational complexity through feature elimination and supervised models, and the Figshare dataset clearly demonstrated the efficacy of our model. This study achieved very exceptional results and reduced computational complexity as compared to a single CNN model. The experimental results indicate that the proposed SVM-RFE based technique accurately detects brain tumors with a 99.51% accuracy and a specificity score of 99.63\%. The proposed approach obtained an accuracy of 98.93% with a standard deviation of 0.032 using 10-fold cross-validation. Additionally, it produced an optimal ROC_AUC of 100% for cases including meningiomas and pituitary tumors.

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“…Moreover, the accuracy of first algorithm was counted 96.45%, 85.03% for second, 89.34% for third and 93.40% for the last algorithm in the multi-class approach. S. Patil, et.al (2023) introduced an Ensemble Deep Convolutional Neural Network Model (EDCNN) framework for detecting the brain tumor [17]. At first, this model employed Shallow Convolutional Neural Network (SCNN) and VGG16 network with 1C modality Magnetic Resonance Imaging (MRI) image.…”

Section: IImentioning

confidence: 99%

“…At first, this model employed Shallow Convolutional Neural Network (SCNN) and VGG16 network with 1C modality Magnetic Resonance Imaging (MRI) image. At second, an analysis was conducted on loss and accuracy [17]. The accuracy was enhanced after fusing the extracted features from utilized models for enhancing the accuracy to classify 3 kinds of brain tumors.…”

Section: IImentioning

confidence: 99%

Analysis of Brain Tumor Detection Techniques

Das,

Jangde,

Bhattacharya

2023

IJRASET

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The bio-medical image processing is the application of image processing to process medical data. The techniques of feature extraction and machine learning are efficient techniques brain tumor detection. Various network architectures are utilized, ordinarily with hundreds or thousands of adjustable parameters. The resulting condition forms are sufficiently general to unravel a large class of nonlinear classification and estimation problems and sufficiently complex to hide a multitude of sins. In this paper, various brain tumor detection techniques are reviewed analyzed in terms of certain parameters. It is analyzed that textual feature extraction and classification techniques give maximum accuracy for the brain tumor detection

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Ensemble of Deep Learning Models for Brain Tumor Detection

Cited by 30 publications

References 13 publications

Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery

Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery

Brain tumor detection through novel feature selection using deep efficientNet-CNN-based features with supervised learning and data augmentation

Analysis of Brain Tumor Detection Techniques

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