Detection of Brain Tumor from the MRI Images using Deep Hybrid Boosted based on Ensemble Techniques

Khan, Mohd Faizaan; Khatri, Pranav; Lenka, Sushant; Anuhya, Dudi; Sanyal, Arka

doi:10.1109/icosec54921.2022.9952062

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Performance Metrics [15] 2021 Hybrid Ensemble Model 97.305% [16] 2022 RCNN and two-channel CNN 97.83% [17] 2022 Deep Hybrid Boosted 95% [18] 2022 TL-CNN 95.75% [19] 2023 CNN models 97.12% Our developed method SVM(LBP and HOG) ~99.9% Modified CNN 98%…”

Section: Year Methodsmentioning

confidence: 99%

“…Deep Hybrid Boosted uses a two-phase deep-learning-based framework suggested by Khan and his colleagues [17] to detect and categorize brain tumors in magnetic resonance images (MRIs); they reached 95% accuracy. Although they did not reach the best accuracy in the field, the Complexity of Hybrid feature fusion methods can be difficult to implement and require careful design and tuning.…”

Section: Literature Reviewmentioning

confidence: 99%

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Brain Pathology Classification of MR Images Using Machine Learning Techniques

Ramaha,

Mahmood,

Hameed

et al. 2023

Computers

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A brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widely recognized as challenging and important tasks. The potential applications of machine learning in diagnostics, preoperative planning, and postoperative evaluations are substantial. Accurate determination of the tumor’s location on a brain MRI is of paramount importance. The advancement of precise machine learning classifiers and other technologies will enable doctors to detect malignancies without requiring invasive procedures on patients. Pre-processing, skull stripping, and tumor segmentation are the steps involved in detecting a brain tumor and measurement (size and form). After a certain period, CNN models get overfitted because of the large number of training images used to train them. That is why this study uses deep CNN to transfer learning. CNN-based Relu architecture and SVM with fused retrieved features via HOG and LPB are used to classify brain MRI tumors (glioma or meningioma). The method’s efficacy is measured in terms of precision, recall, F-measure, and accuracy. This study showed that the accuracy of the SVM with combined LBP with HOG is 97%, and the deep CNN is 98%.

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Section: Year Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Brain Pathology Classification of MR Images Using Machine Learning Techniques

Ramaha,

Mahmood,

Hameed

et al. 2023

Computers

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“…The methodology yielded a confidence level of 98.83 percent on average. M. F. Khan, et al [12] used AdaBoost and random forest algorithms to classify brain tumors. In the related dataset, 95% accuracy was obtained for the AdaBoost algorithm, while 89% accuracy was obtained for the Random Forest algorithm.…”

Section: Literaturementioning

confidence: 99%

Detection and Localization of Glioma and Meningioma Tumors in Brain MR Images using Deep Learning

Cengil

Eroğlu

Çınar

et al. 2023

Sakarya University Journal of Science

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Brain tumors are common tumors arising from parenchymal cells in the brain and the membranes that surround the brain. The most common brain tumors are glioma and meningioma. They can be benign or malignant. Treatment modalities such as surgery and radiotherapy are applied in malignant tumors. Tumors may be very small in the early stages and may be missed by showing findings similar to normal brain parenchyma. The correct determination of the localization of the tumor and its neighborhood with the surrounding vital tissues contributes to the determination of the treatment algorithm. In this paper, we aim to determine the classification and localization of gliomas originating from the parenchymal cells of the brain and meningiomas originating from the membranes surrounding the brain in brain magnetic resonance images using artificial intelligence methods. At first, the two classes of meningioma and glioma tumors of interest are selected in a public dataset. Relevant tumors are then labeled with the object labeling tool. The resulting labeled data is passed through the EfficientNet for feature extraction. Then Path Aggregation Network (PANet) is examined to generate the feature pyramid. Finally, object detection is performed using the detection layer of the You Only Look Once (YOLO) algorithm. The performance of the suggested method is shown with precision, recall and mean Average Precision (mAP) performance metrics. The values obtained are 0.885, 1.0, and 0.856, respectively. In the presented study, meningioma, and glioma, are automatically detected. The results demonstrate that using the proposed method will benefit medical people.

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