Brain Tumor Detection Using Convolutional Neural Network
By Tonmoy Hossain, Fairuz Shadmani Shishir, Mohsena Ashraf, Md. Abdullah Al Nasim, Faisal Muhammad Shah. 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019
305 citations recorded by the source corpus.
Brain Tumor segmentation is one of the most crucial and arduous tasks in the terrain of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it is an aggravating task when there is a large amount of data present to be assisted. Brain tumors have high diversity in appearance and there is a similarity between tumor and normal tissues and thus the extraction of tumor regions from images becomes unyielding. In this paper, we proposed a method to extract brain tumor from 2D Magnetic Resonance brain Images (MRI) by Fuzzy C-Means clustering algorithm which was followed by traditional classifiers and convolutional neural network. The experimental study was carried on a real-time dataset with diverse tumor sizes, locations, shapes, and different image intensities. In traditional classifier part, we applied six traditional classifiers namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Logistic Regression, Naïve Bayes and Random Forest which was implemented in scikit-learn. Afterward, we moved on to Convolutional Neural Network (CNN) which is implemented using Keras and Tensorflow because it yields to a better performance than the traditional ones. In our work, CNN gained an accuracy of 97.87%, which is very compelling. The main aim of this paper is to distinguish between normal and abnormal pixels, based on texture based and statistical based features.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology
View Paper: https://www.semanticscholar.org/paper/f89fe7d194c63a62b2cb76fac08ef525b483b964
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Citations in the corpus
- Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images (2022, methodology): “Several techniques [1] were suggested for the prediction of tumors in the brain.”
- Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging (2023, unknown): “[37] conducted a study that included the examination of several machine learning and deep learning techniques for brain tumor detection and segmentation, including support vector machines, k-nearest neighbors, multi-layer perceptrons, Naive Bayes, and random forest algorithms.”
- Accurate brain tumor detection using deep convolutional neural network (2022, methodology): “Hossain et al., used the Fuzzy C-Means clustering technique to extract the tumor area from the MRI images [31].”