Fig 1 - uploaded by Sunita Kulkarni
Content may be subject to copyright.
Brain Tumor Classification According to AANS.
Contexts in source publication
Context 1
... American Association of Neurological Surgeons (AANS)" has demonstrated the types of tumors according to their nature [1]. Fig. 1 shows the types of tumors. This research aims first to detect the brain tumor from MRI. The tumorous MRIs further classifies using the transfer learning architecture, i.e., AlexNet, into Malignant and benign. The cancerous malignant tumors are also classified into glioma and meningioma using the GoogLeNet architecture of CNN. The ...
Context 2
... quantitative analysis of malignant and benign classification using the AlexNet, Vgg16, ResNet18, ResNet50, and GoogLeNet CNN algorithm is as displayed in Table VI, and its graphical analysis is showing in Fig. ...
Context 3
... analysis performs on the testing dataset for classification of Glioma vs. Meningioma. The results are displaying in Fig. 11. The input samples of the Glioma and Meningioma brain MRI display in Fig. 11(a), and Fig. 11(c) and a resultant class of the GoogLeNet are shown in Fig. 11(b) and Fig. 11(d), respectively. 380 | P a g e www.ijacsa.thesai.org The training progress of GoogLeNet is as displayed in Fig. ...
Context 4
... analysis performs on the testing dataset for classification of Glioma vs. Meningioma. The results are displaying in Fig. 11. The input samples of the Glioma and Meningioma brain MRI display in Fig. 11(a), and Fig. 11(c) and a resultant class of the GoogLeNet are shown in Fig. 11(b) and Fig. 11(d), respectively. 380 | P a g e www.ijacsa.thesai.org The training progress of GoogLeNet is as displayed in Fig. ...
Context 5
... analysis performs on the testing dataset for classification of Glioma vs. Meningioma. The results are displaying in Fig. 11. The input samples of the Glioma and Meningioma brain MRI display in Fig. 11(a), and Fig. 11(c) and a resultant class of the GoogLeNet are shown in Fig. 11(b) and Fig. 11(d), respectively. 380 | P a g e www.ijacsa.thesai.org The training progress of GoogLeNet is as displayed in Fig. ...
Context 6
... analysis performs on the testing dataset for classification of Glioma vs. Meningioma. The results are displaying in Fig. 11. The input samples of the Glioma and Meningioma brain MRI display in Fig. 11(a), and Fig. 11(c) and a resultant class of the GoogLeNet are shown in Fig. 11(b) and Fig. 11(d), respectively. 380 | P a g e www.ijacsa.thesai.org The training progress of GoogLeNet is as displayed in Fig. ...
Context 7
... analysis performs on the testing dataset for classification of Glioma vs. Meningioma. The results are displaying in Fig. 11. The input samples of the Glioma and Meningioma brain MRI display in Fig. 11(a), and Fig. 11(c) and a resultant class of the GoogLeNet are shown in Fig. 11(b) and Fig. 11(d), respectively. 380 | P a g e www.ijacsa.thesai.org The training progress of GoogLeNet is as displayed in Fig. ...
Context 8
... of Glioma vs. Meningioma. The results are displaying in Fig. 11. The input samples of the Glioma and Meningioma brain MRI display in Fig. 11(a), and Fig. 11(c) and a resultant class of the GoogLeNet are shown in Fig. 11(b) and Fig. 11(d), respectively. 380 | P a g e www.ijacsa.thesai.org The training progress of GoogLeNet is as displayed in Fig. ...
Context 9
... quantitative analysis of malignant and benign classification using the AlexNet, GoogLeNet, ResNet18, and ResNet50 CNN algorithm is as displayed in Table VII. The graphical analysis of the proposed approaches with state of the art method is given in Fig. 13. The approach of GoogLeNet is more generalized and shows better accuracy on the testing dataset. The relative analysis of the proposed system with state-of-art approaches shows the dominancy of the proposed GoogLeNet ...
Context 10
... American Association of Neurological Surgeons (AANS)" has demonstrated the types of tumors according to their nature [1]. Fig. 1 shows the types of tumors. This research aims first to detect the brain tumor from MRI. The tumorous MRIs further classifies using the transfer learning architecture, i.e., AlexNet, into Malignant and benign. The cancerous malignant tumors are also classified into glioma and meningioma using the GoogLeNet architecture of CNN. The ...
Context 11
... quantitative analysis of malignant and benign classification using the AlexNet, Vgg16, ResNet18, ResNet50, and GoogLeNet CNN algorithm is as displayed in Table VI, and its graphical analysis is showing in Fig. ...
Context 12
... analysis performs on the testing dataset for classification of Glioma vs. Meningioma. The results are displaying in Fig. 11. The input samples of the Glioma and Meningioma brain MRI display in Fig. 11(a), and Fig. 11(c) and a resultant class of the GoogLeNet are shown in Fig. 11(b) and Fig. 11(d), respectively. 380 | P a g e www.ijacsa.thesai.org The training progress of GoogLeNet is as displayed in Fig. ...
Context 13
... analysis performs on the testing dataset for classification of Glioma vs. Meningioma. The results are displaying in Fig. 11. The input samples of the Glioma and Meningioma brain MRI display in Fig. 11(a), and Fig. 11(c) and a resultant class of the GoogLeNet are shown in Fig. 11(b) and Fig. 11(d), respectively. 380 | P a g e www.ijacsa.thesai.org The training progress of GoogLeNet is as displayed in Fig. ...
Context 14
... analysis performs on the testing dataset for classification of Glioma vs. Meningioma. The results are displaying in Fig. 11. The input samples of the Glioma and Meningioma brain MRI display in Fig. 11(a), and Fig. 11(c) and a resultant class of the GoogLeNet are shown in Fig. 11(b) and Fig. 11(d), respectively. 380 | P a g e www.ijacsa.thesai.org The training progress of GoogLeNet is as displayed in Fig. ...
Context 15
... analysis performs on the testing dataset for classification of Glioma vs. Meningioma. The results are displaying in Fig. 11. The input samples of the Glioma and Meningioma brain MRI display in Fig. 11(a), and Fig. 11(c) and a resultant class of the GoogLeNet are shown in Fig. 11(b) and Fig. 11(d), respectively. 380 | P a g e www.ijacsa.thesai.org The training progress of GoogLeNet is as displayed in Fig. ...
Context 16
... analysis performs on the testing dataset for classification of Glioma vs. Meningioma. The results are displaying in Fig. 11. The input samples of the Glioma and Meningioma brain MRI display in Fig. 11(a), and Fig. 11(c) and a resultant class of the GoogLeNet are shown in Fig. 11(b) and Fig. 11(d), respectively. 380 | P a g e www.ijacsa.thesai.org The training progress of GoogLeNet is as displayed in Fig. ...
Context 17
... of Glioma vs. Meningioma. The results are displaying in Fig. 11. The input samples of the Glioma and Meningioma brain MRI display in Fig. 11(a), and Fig. 11(c) and a resultant class of the GoogLeNet are shown in Fig. 11(b) and Fig. 11(d), respectively. 380 | P a g e www.ijacsa.thesai.org The training progress of GoogLeNet is as displayed in Fig. ...
Context 18
... quantitative analysis of malignant and benign classification using the AlexNet, GoogLeNet, ResNet18, and ResNet50 CNN algorithm is as displayed in Table VII. The graphical analysis of the proposed approaches with state of the art method is given in Fig. 13. The approach of GoogLeNet is more generalized and shows better accuracy on the testing dataset. The relative analysis of the proposed system with state-of-art approaches shows the dominancy of the proposed GoogLeNet ...
Similar publications
Explainability in medical images analysis plays an important role in the accurate diagnosis and treatment of tumors, which can help medical professionals better understand the images analysis results based on deep models. This paper proposes an explainable brain tumor detection framework that can complete the tasks of segmentation, classification,...
Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition. Brain tumor segmentation is the process of algorithmically identifying tumors in brain MRI scans. While many approaches have been proposed in the literature for brain tumor segmentation, this paper proposes a lightweight implementation...
Citations
... Over the past decades, significant discoveries have been made in the field of medical image analysis, particularly in the development of sophisticated algorithms and techniques for brain tumor classification and segmentation [4]. These advancements have been primarily driven by the increasing availability of large-scale datasets, improvements in computational resources and advancements in machine learning and deep learning methodologies [5]. ...
Brain tumors are one of the most aggressive disease. They are caused when abnormal cells are formed and spread from uncontrolled cell division. They can be identified and classified using convolutional neural networks for medical image analysis. The study emphasizes the importance of accurate diagnosis and treatment planning in brain tumor management, facilitated by imaging techniques such as Magnetic Resonance Imaging (MRI). Using the power of CNNs, The research focuses on classifying and segmenting brain tumors from raw MRI images, employing sophisticated algorithms and techniques. The methodology involves utilizing U-Net architecture, known for its effectiveness in segmentation, to outline brain tumors precisely. The study uses two distinct datasets comprising a total of 3455 MRI images sourced from Kaggle and curated by domain experts. Through rigorous expert-imitation and training, the CNN model demonstrates promising results in accurately identifying and classifying brain tumor. The training process involves dataset preprocessing, division into training, validation and testing sets, and optimization using the Adam optimizer. Evaluation metrics such as accuracy, loss curves, confusion matrices and Receiver Operating Characteristics (ROC) curve validates the effectiveness of the model without overfitting. The findings contribute to the growing body of knowledge in medical image processing, offering a viable approach to enhance brain tumor detection and therapy.
... Over the past decades, significant discoveries have been made in the field of medical image analysis, particularly in the development of sophisticated algorithms and techniques for brain tumor classification and segmentation [4]. These advancements have been primarily driven by the increasing availability of large-scale datasets, improvements in computational resources and advancements in machine learning and deep learning methodologies [5]. ...
Brain tumors are one of the most aggressive disease. They are caused when abnormal cells are formed and spread from uncontrolled cell division. They can be identified and classified using convolutional neural networks for medical image analysis. The study emphasizes the importance of accurate diagnosis and treatment planning in brain tumor management, facilitated by imaging techniques such as Magnetic Resonance Imaging (MRI). Using the power of CNNs, The research focuses on classifying and segmenting brain tumors from raw MRI images, employing sophisticated algorithms and techniques. The methodology involves utilizing U-Net architecture, known for its effectiveness in segmentation, to outline brain tumors precisely. The study uses two distinct datasets comprising a total of 3455 MRI images sourced from Kaggle and curated by domain experts. Through rigorous expert-imitation and training, the CNN model demonstrates promising results in accurately identifying and classifying brain tumor. The training process involves dataset preprocessing, division into training, validation and testing sets, and optimization using the Adam optimizer. Evaluation metrics such as accuracy, loss curves, confusion matrices and Receiver Operating Characteristics (ROC) curve validates the effectiveness of the model without overfitting. The findings contribute to the growing body of knowledge in medical image processing, offering a viable approach to enhance brain tumor detection and therapy.
... In order to give medical professionals enough information for clinical objectives [1]. One of the main health issues connected to anomalies [2] in the human brain is [3] a brain tumor [4]. These brain tissues play an important role in the process of brain tumor classification. ...
Brain diseases are primarily brought on by abnormal brain cell growth, which can harm the structure of the brain and eventually result in malignant brain cancer. Major challenges exist when using a computer aided diagnosis (CAD) system for an early diagnosis that enables decisive treatment, particularly when it comes to the accurate detection of various diseases in the pictures for magnetic resonance imaging (MRI). In this study, the fuzzy convolutional neural networks (FCNN) were proposed for accurate diagnosis of brain tumors (glioma, meningioma, pituitary and non-tumor) which is implemented using Keras and TensorFlow. This approach follows three steps, training, testing, and evaluation. In training process, it builds a smart model and the structure consists of seven blocks (convolution, rectified linear unit (ReLU), batch normalization, and max pooling) then use flatten, fuzzy inferences layer, and dense layer with dropout. An international dataset with 7,022 brain tumor MRI images, was tested. The evaluation model attained a high performance with training accuracy of 99.84% and validation accuracy is 98.63% with low complexity and time is 58 s per epoch. The suggested approach performs better than the other known algorithms and may be quickly and accurately used for medical picture diagnosis.
... 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. ...
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.
... In this research paper, the authors proposed another deep-learning protocol for brain cell tissue classifications. In this suggested article brain cells tissues monitoring procedure was done via preprocessing as well as the skull stripping in real-time for evaluation of the brain tissue's proper segmentation [43]. ...
Brain tumor detection in the initial stage is becoming an intricate task for clinicians worldwide. The diagnosis of brain tumor patients is rigorous in the later stages, which is a serious concern. Although there are related pragmatic clinical tools and multiple models based on machine learning (ML) for the effective diagnosis of patients, these models still provide less accuracy and take immense time for patient screening during the diagnosis process. Hence, there is still a need to develop a more precise model for more accurate screening of patients to detect brain tumors in the beginning stages and aid clinicians in diagnosis, making the brain tumor assessment more reliable. In this research, a performance analysis of the impact of different generative adversarial networks (GAN) on the early detection of brain tumors is presented. Based on it, a novel hybrid enhanced predictive convolution neural network (CNN) model using a hybrid GAN ensemble is proposed. Brain tumor image data is augmented using a GAN ensemble, which is fed for classification using a hybrid modulated CNN technique. The outcome is generated through a soft voting approach where the final prediction is based on the GAN, which computes the highest value for different performance metrics. This analysis demonstrated that evaluation with a progressive-growing generative adversarial network (PGGAN) architecture produced the best result. In the analysis, PGGAN outperformed others, computing the accuracy, precision, recall, F1-score, and negative predictive value (NPV) to be 98.85, 98.45%, 97.2%, 98.11%, and 98.09%, respectively. Additionally, a very low latency of 3.4 s is determined with PGGAN. The PGGAN model enhanced the overall performance of the identification of brain cell tissues in real time. Therefore, it may be inferred to suggest that brain tumor detection in patients using PGGAN augmentation with the proposed modulated CNN technique generates the optimum performance using the soft voting approach.
... 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. ...
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.
... G.Sundari et al. [6] proposed a brain tumor classification system in which pre-processing of the MRI images includes skull stripping, followed by morphological procedures. Methods of deep learning and transfer learning are employed. ...
... G.Sundari et al. [5] proposed a brain tumor classification system in which pre-processing of the MRI images includes skull stripping, followed by morphological procedures. Methods of deep learning and transfer learning are employed. ...
The detection of brain tumors using Magnetic Resonance Images(MRI) is difficult for contemporary medical imaging research. Basically, a brain tumor is an expansion of aberrant brain cells that expand erratically and seemingly uncontrolled. Meningioma, Glioma, and Pituitary are the three kinds of tumors that are most frequently seen. Early identification is essential for the successful treatment of brain tumors. With the development of medical imaging, doctors now employ a variety of imaging methods, such as fMRI, EEG, etc., to diagnose brain tumors. These imaging methods can help clinicians establish a precise diagnosis and create a treatment strategy by providing details on the location, size, and shape of brain tumors. Feature extraction and classification are two steps in the categorization of brain tumors. Two traditional manual feature extraction methods were frequently utilized in certain earlier research to extract details like the intensity and texture of images of brain tumors. This work employs the 'GLCM(Grey Level Co-occurrence Matrix)' approach for feature extraction. The generated feature set is provided to machine learning(ML) algorithms, including 'K-Nearest Neighbours(KNN), Support Vector Machine(SVM), Decision Tree(DT), Naive Bayes(NB), Logistic Regression(LR), Naive Bayes(NB), and Random Forest(RF)'. According to experimental results, random forest yields a maximum accuracy of 91.04%. The proposed methodology helps to classify the different brain tumor classes like glioma, meningioma, pituitary, or else no tumor.
... 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. ...
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.
... The application of CNN's integrated feature extraction and classification to recognize and identify brain Tumors is suggested in the paper [7][8][9][10]. An artificial convolutional neural network serves as the classification model for tumor detection systems. ...
Brain tumour detection is essential for improving patient survival and prospects. This research work necessitates a physical examination with magnetic resonance imaging (MRI). As a result, computational algorithms are required for more accurate tumour diagnosis. Moreover, evaluating shape, boundaries, volume, size, segmentation, tumour detection, and classification remains difficult. To resolve these problems, hybrid deep convolutional neural network (DCNN) with enhanced LuNet classifier algorithm has been proposed for brain tumour detection. The main intention of the proposed approach is to locate the tumor and classify brain tumors as Glioma or Meningioma. For preprocessing, a Laplacian Gaussian filter (LOG) is used. A Fuzzy C Means with Gaussian mixture model (FCM-GMM) algorithm has been proposed for segmentation. To begin, use the extended LuNet algorithm to divide the data. A VGG16 extraction feature yields thirteen categorical features. Overall, the proposed method attempts to improve the performance of classifiers. The proposed LuNet classifiers are an excellent deep learning technique because it has low computational complexity, are inexpensive, and are simple to use even for those with little training experience. The simulated outcomes of the proposed algorithm compared to other conventional algorithms like SVM, Decision tree, Random forest, Alexnet, Resnet-50 and Googlenet classifier algorithm. The introduced hybrid approach achieves 99.7% accuracy. When compared to other existing algorithms, the proposed method outperforms them.
