Predictive modeling of brain tumor: A Deep learning approach

Saxena, Priyansh; Maheshwari, Akshat; Tayal, Shivani; Maheshwari, Saumil

doi:10.48550/arxiv.1911.02265

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Saxena et al [23] has also implemented and proposed resNet 50 for brain MRI images classification. They compared VGG16 and inceptionV3, however, the resNet50 has outperformed both model with the 95% accuracy.…”

Section: Resultsmentioning

confidence: 99%

Improving the performance for automated brain tumor classification on magnetic resonance imaging deep learningbased

Fachrurrozi,

Darmawahyuni,

Samsuryadi

et al. 2024

IJ-AI

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<p>Brain tumor is an uncontrolled growth of abnormal cell in the brain. Early diagnosis of brain tumor has a crucial step in this type of cancer, which is fatal. Magnetic resonance imaging (MRI) is one of the examination tools to examine brain anatomy in clinical practice. The high resolution and clear separation of the tissue enable medical experts to identify brain tumor. The earlier of brain tumor is detected, the wider of treatment options. However, manually analysed of brain anatomy on MRI images are time-consuming. Computer-aided diagnosis with automated way is helpful solution to help management with unreliable degrees of automation to trace various tissue boundaries. This study proposes convolutional neural network (CNN) with its excellences to automated features extraction in convolution layer. The popular architectures of CNN, i.e., visual geometry group16 (VGG16), residual network-50 (resNet-50), inceptionV3, mobileNet, and efficientNetB7 in medical image processing are compared to brain tumor classification task. As the results, VGG16 outperformed other architectures of CNN in this study. VGG16 yields 100% accuracy, precision, sensitivity, specificity, and F1-score for testing set data. The results show the excellent performance in classifying brain tumor and no tumor from MRI images that demonstrate the efficiency of system suggested.</p>

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Section: Resultsmentioning

confidence: 99%

Improving the performance for automated brain tumor classification on magnetic resonance imaging deep learningbased

Fachrurrozi,

Darmawahyuni,

Samsuryadi

et al. 2024

IJ-AI

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“…We evaluate the comparative performance analysis of SA-CNN and other studies on Kaggle database. We compare the Eclipse + KNN [38], Parabola + SVM [38], Hyperbola + SVM [38], VGG 16 [39], Res-Net [39], Inception V3 [39], CNN [40] and two different CNN Models [41] with our proposed SA-CNN. The accuracy of all the models on Kaggle database is reported in Table 8.…”

Section: Comparative Evaluation With State-of-the-art Techniques On K...mentioning

confidence: 99%

Computer Aided Brain Tumor Diagnosis Network Employing Structure-Aware CNNs

Singh

Goel

Raghuvanshi

2022

Preprint

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Brain tumor is one of the deadliest cancers diagnosed in humans. Accurate and early detection of brain tumors play pivotal role in analysing and planning its treatment accordingly. Therefore, development of automated assisted framework for brain tumor detection and classification becomes very important. This paper presents structure aware CNN (SA-CNN) for binary brain tumor classification. The proposed SA-CNN extracts discriminant and significant image features from brain tumor images. We develop structure aware convolution (SAC) filters by generalizing the filters in traditional convolution to capture structure information which equips the network with the capability to deal with datasets having Euclidean and non-Euclidean structures. We explore classification capabilities of other pre-trained deep convolutional neural networks, namely Alex-Net, VGG 19, Inception V1, Res-Net 34 and Dense-Net 161 for comparative analysis. The proposed network is more generalized as it does not rely on hand-crafted features maps and achieves better performance. Extensive experiments on benchmark Kaggle dataset and comparative analysis suggests that the proffered framework outperforms state-of-the-art (SOTA) techniques. The proposed framework achieves accuracy of 99.54% and 98.04 % on training dataset and test dataset respectively.

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“… Abd-Ellah et al (2020) improved a deep CNN architecture for brain tumor detection, reaching 97.79% accuracy. Saxena et al (2019) applied transfer learning approaches to Inception V3, ResNet-50, and VGG-16 models, achieving the best accuracy rate of 95%. Khan et al (2020) used deep transfer learning methods to classify brain tumors from MRI scans, improving the VGG-16 model using the Brain Tumor Segmentation (BraTS) dataset and achieving an accuracy of 96.27%.…”

Section: Introductionmentioning

confidence: 99%

A novel approach to brain tumor detection using K-Means++, SGLDM, ResNet50, and synthetic data augmentation

Sarah,

Krishnapriya,

Saladi

et al. 2024

Front. Physiol.

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Introduction: Brain tumors are abnormal cell growths in the brain, posing significant treatment challenges. Accurate early detection using non-invasive methods is crucial for effective treatment. This research focuses on improving the early detection of brain tumors in MRI images through advanced deep-learning techniques. The primary goal is to identify the most effective deep-learning model for classifying brain tumors from MRI data, enhancing diagnostic accuracy and reliability.Methods: The proposed method for brain tumor classification integrates segmentation using K-means++, feature extraction from the Spatial Gray Level Dependence Matrix (SGLDM), and classification with ResNet50, along with synthetic data augmentation to enhance model robustness. Segmentation isolates tumor regions, while SGLDM captures critical texture information. The ResNet50 model then classifies the tumors accurately. To further improve the interpretability of the classification results, Grad-CAM is employed, providing visual explanations by highlighting influential regions in the MRI images.Result: In terms of accuracy, sensitivity, and specificity, the evaluation on the Br35H::BrainTumorDetection2020 dataset showed superior performance of the suggested method compared to existing state-of-the-art approaches. This indicates its effectiveness in achieving higher precision in identifying and classifying brain tumors from MRI data, showcasing advancements in diagnostic reliability and efficacy.Discussion: The superior performance of the suggested method indicates its robustness in accurately classifying brain tumors from MRI images, achieving higher accuracy, sensitivity, and specificity compared to existing methods. The method's enhanced sensitivity ensures a greater detection rate of true positive cases, while its improved specificity reduces false positives, thereby optimizing clinical decision-making and patient care in neuro-oncology.

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Predictive modeling of brain tumor: A Deep learning approach

Cited by 5 publications

References 11 publications

Improving the performance for automated brain tumor classification on magnetic resonance imaging deep learningbased

Improving the performance for automated brain tumor classification on magnetic resonance imaging deep learningbased

Computer Aided Brain Tumor Diagnosis Network Employing Structure-Aware CNNs

A novel approach to brain tumor detection using K-Means++, SGLDM, ResNet50, and synthetic data augmentation

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