Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification

A Two-Dimensional Convolutional Neural Network for Brain Tumor Detection From MRI

Aims: Cancerous brain tumors are among the most dangerous diseases that lower the quality of life of people for many years. Their detection in the early stages paves the way for the proper treatment. The present study aimed to present a two-dimensional Convolutional Neural Network (CNN) for detecting brain tumors under Magnetic Resonance Imaging (MRI) using the deep learning method. Methods & ...

Convolutional Neural Network Visualization for fMRI Brain Disease Classification Tasks

A Convolutional Neural Network based on Adaptive Pooling for Classification of Noisy Images

Convolutional neural network is one of the effective methods for classifying images that performs learning using convolutional, pooling and fully-connected layers. All kinds of noise disrupt the operation of this network. Noise images reduce classification accuracy and increase convolutional neural network training time. Noise is an unwanted signal that destroys the original signal. Noise chang...

A hierarchical Convolutional Neural Network for Segmentation of Stroke Lesion in 3D Brain MRI

Introduction: Brain tumors such as glioma are among the most aggressive lesions, which result in a very short life expectancy in patients. Image segmentation is highly essential in medical image analysis with applications, particularly in clinical practices to treat brain tumors. Accurate segmentation of magnetic resonance data is crucial for diagnostic purposes, planning surgical treatments, a...

A hierarchical Convolutional Neural Network for Segmentation of Stroke Lesion in 3D Brain MRI

Introduction: Brain tumors such as glioma are among the most aggressive lesions, which result in a very short life expectancy in patients. Image segmentation is highly essential in medical image analysis with applications, particularly in clinical practices to treat brain tumors. Accurate segmentation of magnetic resonance data is crucial for diagnostic purposes, planning surgical treatments, a...