Brain Tumor Classification Using Convolutional Neural Network

By Nyoman Abiwinanda, M. Hanif, S. Tafwida Hesaputra, Astri Handayani, T. Mengko. IFMBE Proceedings, 2018

585 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/fbbd3381640dae2ca4b03ac7ed85bfec896929b8

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In Computer Vision and Pattern Recognition

In Neurology

Citations in the corpus

A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification (2019, background): “In [12-14], the authors introduce a CNN architecture to classify brain tumors.”
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network (2021, methodology): “[24] used two convolution layers and 64 fully connected neurons.”
MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers (2021, methodology): “The deep learning method requires a dataset where sometimes a pre-processing operation needs to be done, and then salient features are determined in a self-learning manner [25].”
Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey (2020, unknown): “for both segmentation and classification [43]–[47].”
A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor (2020, methodology): “[20] adopted a deep learning model, that was based on CNN, applied to brain tumor images classification.”
MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques (2023, background): “The best-reported accuracy rates for training and validation were 98.51% and 84.19%, respectively [28].”
Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images (2022, methodology): “CNN was realized as the first real-world application in 1998 to observe handwritten digits [10].”
Brain tumor classification using deep CNN features via transfer learning (2019, unknown): “A CNN-based deep learning model was successfully applied to the considered brain tumor classification problem [20].”
Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging (2023, methodology): “[93] continued along a similar path by creating a CNN model for tumor classification, using just 3064 MRI images from the Figshare dataset.”
Deep CNN for Brain Tumor Classification (2021, methodology): “Some other works that used CNN [33, 37] have exploited shallow networks with a few MRI data, which lead to lower accuracy value.”