Brain Tumor Detection Using Convolutional Neural Network

Hossain, Tonmoy; Shishir, Fairuz Shadmani; Ashraf, Mohsena; Nasim, M. D. Abdullah Al; Shah, Faisal Muhammad

doi:10.1109/icasert.2019.8934561

Cited by 200 publications

(56 citation statements)

References 4 publications

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“…Bhandari et al [13], Introduced a process to abstract brain tumors from 2D MRI brain using a Fuzzy C-Means grouping procedure followed by classical algorithms and CNN. The empirical research was conducted on an actual time dataset with different cancer dimensions, places, patterns, and various image strengths.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

Basir

Shantta

2021

IRA-JAS

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Deep Learning is a growing field of artificial intelligence that has become an operative research topic in a wide range of disciplines. Today we are witnessing the tangible successes of Deep Learning in our daily lives in various applications, including education, manufacturing, transportation, healthcare, military, and automotive, etc.<strong> </strong>Deep Learning is a subfield of Machine Learning that stems from Artificial Neural Networks, where a cascade of layers is employed to progressively extract higher-level features from the raw input and make predictive guesses about new data. This paper will discuss the effect of attribute extraction profoundly inherent in training<strong> </strong>approaches such as Convolutional Neural Networks (CNN). Furthermore, the paper aims to offer a study on Deep Learning techniques and attribute extraction methods that have appeared in the last few years. As the demand increases, considerable research in the attribute extraction assignment has become even more instrumental. Brain tumor characterization and detection will be used as a case study to demonstrate Deep Learning CNN's ability to achieve effective representational learning and tumor characterization.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

Basir

Shantta

2021

IRA-JAS

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“…This work presented the accuracy of 96.56% in tumor classification from MRI images. Hossain et al [14] proposed a method to extract brain tumor from MRI images using Fuzzy C-mean clustering algorithm followed by CNN model. This work was implemented in keras & tensor flow and gained an accuracy of 97.87%.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Neural Networks with Transfer Learning Model for Brain Tumors Classification

Premamayudu¹,

Anantha²,

Subbarao³

2020

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To investigate the effect of deep neural networks with transfer learning on MR images for tumor classification and improve the classification metrics by building image-level, stratified image-level, and patient-level models. Three thousand sixty-four T1-weighted magnetic resonance (MR) imaging from two hundred thirty-three patient cases of three brain tumors types (meningioma, glioma, and pituitary) were collected and it includes coronal, sagittal and axial views. The average number of brain images of each patient in three views is fourteen in the collected dataset. The classification is performed in a model of cross-trained with a pre-trained InceptionV3 model. Three image-level and one patient-level models are built on the MR imaging dataset. The models are evaluated in classification metrics such as accuracy, loss, precision, recall, kappa, and AUC. The proposed models are validated using four approaches: holdout validation, 10-fold cross-validation, stratified 10-fold cross-validation, and group 10-fold cross-validation. The generalization capability and improvement of the network are tested by using cropped and uncropped images of the dataset. The best results for group 10-fold cross-validation (patient-level) are obtained on the used dataset (ACC=99.82). A deep neural network with transfer learning can be used to classify brain tumors from MR images. Our patient-level network model noted the best results in classification to improve accuracy.

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“…T. Hossain, F. Shishir, M. Ashraf, M. Al Nasim and F. Muhammad Shah [15] applied two different approaches for segmentation and detection of Brain tumor. First model segmented the tumor by FCM and classified it by traditional machine learning algorithms and the second model focused on deep learning for tumor detection.…”

Section: Figure 1 Digital Image Processing Systemmentioning

confidence: 99%