scholarly journals Description and classification of normal and pathological aging processes based on brain magnetic resonance imaging morphology measures

2014 ◽  
Vol 1 (3) ◽  
pp. 034002 ◽  
Author(s):  
Jorge Luis Perez-Gonzalez ◽  
Oscar Yanez-Suarez ◽  
Ernesto Bribiesca ◽  
Fernando Arámbula Cosío ◽  
Juan Ramón Jiménez ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Pathological Aging

scholarly journals Accurate Classification of Chronic Migraine via Brain Magnetic Resonance Imaging

2015 ◽  
Vol 55 (6) ◽  
pp. 762-777 ◽  
Author(s):  
Todd J. Schwedt ◽  
Catherine D. Chong ◽  
Teresa Wu ◽  
Nathan Gaw ◽  
Yinlin Fu ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Chronic Migraine ◽  
Brain Magnetic Resonance Imaging ◽  
Resonance Imaging

Survey of denoising, segmentation and classification of magnetic resonance imaging for prostate cancer

2021 ◽  
Author(s):  
Mamta Juneja ◽  
Sumindar Kaur Saini ◽  
Jatin Gupta ◽  
Poojita Garg ◽  
Niharika Thakur ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Resonance Imaging

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

Sign in / Sign up

Export Citation Format

Share Document