A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no control over tumor growth. Deep learning has been argued to have the potential to overcome the challenges associated with detecting and intervening in brain tumors. It is well established that the segmentation method can be used to remove abnormal tumor regions from the brain, as this is one of the advanced technological classification and detection tools. In the case of brain tumors, early disease detection can be achieved effectively using reliable advanced A.I. and Neural Network classification algorithms. This study aimed to critically analyze the proposed literature solutions, use the Visual Geometry Group (VGG 16) for discovering brain tumors, implement a convolutional neural network (CNN) model framework, and set parameters to train the model for this challenge. VGG is used as one of the highest-performing CNN models because of its simplicity. Furthermore, the study developed an effective approach to detect brain tumors using MRI to aid in making quick, efficient, and precise decisions. Faster CNN used the VGG 16 architecture as a primary network to generate convolutional feature maps, then classified these to yield tumor region suggestions. The prediction accuracy was used to assess performance. Our suggested methodology was evaluated on a dataset for brain tumor diagnosis using MR images comprising 253 MRI brain images, with 155 showing tumors. Our approach could identify brain tumors in MR images. In the testing data, the algorithm outperformed the current conventional approaches for detecting brain tumors (Precision = 96%, 98.15%, 98.41% and F1-score = 91.78%, 92.6% and 91.29% respectively) and achieved an excellent accuracy of CNN 96%, VGG 16 98.5% and Ensemble Model 98.14%. The study also presents future recommendations regarding the proposed research work.

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Brain Tumor Analysis Using Deep Learning and VGG-16 Ensembling Learning Approaches

Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools

The classification of the brain tumor image is playing a vital role in the medical image domain, and it directly assists the clinicians to understand the severity and to take an appropriate solution. The magnetic resonance imaging tool is used to analyze the brain tissues and to examine the different portion of brain circumstance. We propose the convolutional neural network database learning along with neighboring network limitation (CDBLNL) technique for brain tumor image classification in medical image processing domain. The proposed system architecture is constructed with multilayer-based metadata learning, and they have integrated with CNN layer to deliver the accurate information. The metadata-based vector encoding is used, and the type of coding estimation for extra dimension is known as sparse. In order to maintain the supervised data in terms of geometric format, the atoms of neighboring limitation are built based on a well-structured k-neighbored network. The resultant of the proposed system is considerably strong and subjective for classification. The proposed system used two different datasets, such as BRATS and REMBRANDT, and the proposed brain MRI classification technique outcome is more efficient than the other existing techniques.

/pdf/computational-and-mathematical-methods-in-medicine-glioma-568dqhz5.pdf

Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network

Stroke is the third leading cause of death in the world. It is a dangerous health disorder caused by the interruption of the blood flow to the brain, resulting in severe illness, disability, or death. An accurate prediction of stroke is necessary for the early stage of treatment and overcoming the mortality rate. This study proposes a machine learning approach to diagnose stroke with imbalanced data more accurately. Random Over Sampling (ROS) technique has been used in this work to balance the data. Eleven classifiers, including Support Vector Machine, Random Forest, K-nearest Neighbor, Decision Tree, Naïve Bayes, Voting Classifier, AdaBoost, Gradient Boosting, Multi-Layer Perception, and Nearest Centroid, are analyzed in this study. Ten classifiers show more than 90% accurate results before balancing the data and four classifiers display more than 96% accurate results after data-balancing using the oversampling method. The Hyperparameter tuning and cross-validation are performed in each model to enhance the results. Moreover, Accuracy, F1-Measure, Precision, and Recall are used to measure the performance of machine learning models. The results show the Support Vector Machine has the highest accuracy of 99.99%, with recall values of 99.99%, precision values of 99.99%, and F1-measure of 99.99%. Random Forest achieves the second-highest accuracy of 99.87%, with a 0.001% error. In addition, a user-friendly web app and a user-friendly mobile app are built based on the most accurate model. 

Arkapravo Chattopadhyay

Papers

MRI-based Brain Tumor Image Detection Using CNN based Deep Learning Method