A Framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm

Kulkarni, Sunita M.; Sundari, G.

doi:10.14569/ijacsa.2020.0110848

Cited by 20 publications

(10 citation statements)

References 13 publications

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“…Many nonbinary classification approaches have emerged based on the location of tumors inside the brain. These include SVM (support vector machines), GoogLeNet, ANN (Artificial Neural Network), AlexNet, VGG 16, FCM, Inception V3 model, and ResNet 50 [8,9]. CNN is a multilayered, interconnected Perceptron.…”

Section: Related Workmentioning

confidence: 99%

CE-EEN-B0: Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images

Mahesh¹,

Banerjee²,

Saha³

et al. 2023

Computers, Materials &Amp; Continua

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A brain tumor is the uncharacteristic progression of tissues in the brain. These are very deadly, and if it is not diagnosed at an early stage, it might shorten the affected patient's life span. Hence, their classification and detection play a critical role in treatment. Traditional Brain tumor detection is done by biopsy which is quite challenging. It is usually not preferred at an early stage of the disease. The detection involves Magnetic Resonance Imaging (MRI), which is essential for evaluating the tumor. This paper aims to identify and detect brain tumors based on their location in the brain. In order to achieve this, the paper proposes a model that uses an extended deep Convolutional Neural Network (CNN) named Contour Extraction based Extended EfficientNet-B0 (CE-EEN-B0) which is a feed-forward neural network with the efficient net layers; three convolutional layers and max-pooling layers; and finally, the global average pooling layer. The site of tumors in the brain is one feature that determines its effect on the functioning of an individual. Thus, this CNN architecture classifies brain tumors into four categories: No tumor, Pituitary tumor, Meningioma tumor, and Glioma tumor. This network provides an accuracy of 97.24%, a precision of 96.65%, and an F1 score of 96.86% which is better than already existing pre-trained networks and aims to help health professionals to cross-diagnose an MRI image. This model will undoubtedly reduce the complications in detection and aid radiologists without taking invasive steps.

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Section: Related Workmentioning

confidence: 99%

CE-EEN-B0: Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images

Mahesh¹,

Banerjee²,

Saha³

et al. 2023

Computers, Materials &Amp; Continua

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“…Author's methodology outperformed the other methods that used the same database by achieving a remarkable 0.973 accuracy in tumor classification. A brain tumor detection and classification approach has been available since [28]. The steps of pre-processing, skull stripping, as well as tumor segmentation is used in diagnosing brain tumors.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Attention based MobileNetv2 Model for Brain Tumor Segmentation and Severity of Tumor Classification using Support Vector Machine

Pavithra,

Nidhya,

Vinothini

et al. 2023

Preprint

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Brain tumors are lumps of aberrant tissue that can develop into cancer and have a significant negative influence on a person's health. MRI scans of the brain can reveal them. Segmentation and classification are two elements in these approaches that are extremely crucial. As opposed to anatomical organ segmentation, tumor segmentation is much more difficult due to the variety in size, location, and shape of tumors. For this reason, it is imperative to build reliable, precise, and effective deep learning-based methods. Recent deep learning techniques for classifying and segmenting brain tumors produced encouraging results. These approaches, however, have heavy-weight architectures by nature, necessitating more storage and costly training procedures because of the enormous number of training parameters they must be fed. It is crucial to investigate transportable deep learning models without compromising classification precision. In this research, we provide compact deep neural network models using the pre-trained Attentiveness MobileNetV2 models along with the attention module. The four phases of the proposed system are preliminary processing, division, extracting and categorizing features, and severity classification. Anisotropic diffusion processing as well as data enhancement methods are used initially. The tumor region is then segmented using the proposed modified dimensional U-Net (3D-M-U-Net). Finally, the extraction and classification of features are implemented using the Compact MobileNetV2 framework. Here, the high-level tumor-based information is initially recovered from the convolution features. The important semantic information is then captured using an attention module. Once high-level tumor-based data as well as fascinating semantic information have been combined in the convolutional and focused modules, fully linked layers as well as the layer of softmax are utilized to categorize tumours into either benign or dangerous. Finally, Support Vector Machine (SVM) is used to categorize tumors into moderate, severe, and mild phases. The suggested approach was tested on the high-quality brain cancer images available in the Brats-2020 as well as Brats-2019 datasets. In regards to precision, recall, accuracy, F-Score, Dice Similarities Coefficient (DSC), as well as Structural Similarity Indicator Matrix (SSIM), the suggested model outperforms existing traditional and hybrid models. It was also the most effective and productive method tested. The suggested model has a 99.9% accuracy, a 99.9% precision, and a 99.8% recall across both datasets.

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“…They have applied transfer learning and fine-tuning approaches to the YOLO model to identify three types of brain tumors from MRI images. S. M. Kulkarni et al [25] conducted the research in 2020. They constructed a work set to separate and classify brain tumors using a deep learning approach involving CNN models and AlexNet architecture coupled with transfer learning based on GoogLeNet architecture.…”

Section: Introductionmentioning

confidence: 99%

A Transfer Learning-Based Model for Brain Tumor Detection in MRI Images

Faiz Rofi Hencya,

Satria Mandala,

Tong Boon Tang

et al. 2023

JNTE

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Brain tumors are life-threatening medical conditions characterized by abnormal cell proliferation in or near the brain. Early detection is crucial for successful treatment. However, the scarcity of labelled brain tumor datasets and the tendency of convolutional neural networks (CNNs) to overfit on small datasets have made it challenging to train accurate deep learning models for brain tumor detection. Transfer learning is a machine learning technique that allows a model trained on one task to be reused for a different task. This approach is effective in brain tumor detection as it allows CNNs to be trained on larger datasets and generalize better to new data. In this research, we propose a transfer learning approach using the Xception model to detect four types of brain tumors: meningioma, pituitary, glioma, and no tumor (healthy brain). The performance of our model was evaluated on two datasets, demonstrating a sensitivity of 98.07%, specificity of 97.83%, accuracy of 98.15%, precision of 98.07%, and f1-score of 98.07%. Additionally, we developed a user-friendly prototype application for easy access to the Xception model for brain tumor detection. The prototype was evaluated on a separate dataset, and the results showed a sensitivity of 95.30%, specificity of 96.07%, accuracy of 95.30%, precision of 95.31%, and f1-score of 95.27%. These results suggest that the Xception model is a promising approach for brain tumor detection. The prototype application provides a convenient and easy-to-use way for clinical practitioners and radiologists to access the model. We believe the model and prototype generated from this research will be valuable tools for diagnosing, quantifying, and monitoring brain tumors.

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A Framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm

Cited by 20 publications

References 13 publications

CE-EEN-B0: Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images

CE-EEN-B0: Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images

A Lightweight Attention based MobileNetv2 Model for Brain Tumor Segmentation and Severity of Tumor Classification using Support Vector Machine

A Transfer Learning-Based Model for Brain Tumor Detection in MRI Images

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