Magnetic resonance imaging diagnosis of brain tumors in dogs

2015 ◽  
Vol 205 (2) ◽  
pp. 204-216 ◽  
Author(s):  
R. Timothy Bentley
2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  

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.


2006 ◽  
Vol 48 (3) ◽  
pp. 150-159 ◽  
Author(s):  
N. Rollin ◽  
J. Guyotat ◽  
N. Streichenberger ◽  
J. Honnorat ◽  
V.-A. Tran Minh ◽  
...  

Radiology ◽  
1984 ◽  
Vol 150 (1) ◽  
pp. 95-98 ◽  
Author(s):  
T Araki ◽  
T Inouye ◽  
H Suzuki ◽  
T Machida ◽  
M Iio

2010 ◽  
Vol 52 (2) ◽  
pp. 188-191 ◽  
Author(s):  
BUNITA M. EICHELBERGER ◽  
SUSAN L. KRAFT ◽  
CHARLES H. C. HALSEY ◽  
RICHARD D. PARK ◽  
MATTHEW D. MILLER ◽  
...  

1992 ◽  
Vol 15 (1) ◽  
pp. 22-25 ◽  
Author(s):  
Carlo Masciocchi ◽  
Claudio D'Archivio ◽  
Antonio Barile ◽  
Eva Fascetti ◽  
Bruno Beomonte Zobel ◽  
...  

Author(s):  
I. Shirazu ◽  
Y. B Mensah ◽  
T. A Sackey ◽  
M. Boadu ◽  
E K Eduful ◽  
...  

Physical imaging technique described as Diffusion Weighted-Magnetic Resonance Imaging (DW-MRI) is based on classically principle of Brownian motion, where the molecules are thermal agitated and is highly influenced by the cellular availability of water. The aim of this study is to discuss the use of DW-MRI as a cancer diagnostic application tool using the basic physics principles as versus other available procedures and modalities in terms of accuracy and acceptability. Based on extravascular diffusion measurements where the measured signal is related to tissue cellularity, tissue organization and extracellular space tortuosity and on the intactness of cellular membranes that are intrinsically hydrophobic. The methodology involve the application of DW-MRI procedure, to qualitatively and quantitatively access DW-MR images to diagnose brain tumors, prostate and other organ cancers compared to other imaging modalities including other MRI procedures. It also include safety assessment and other consideration before, during and after imaging with MRI as compare to other radiological modalities. The results of the data of ten (10) MRI centers and 112 DW-MRI images and 99 other procedure and modalities were analysed, 34% were prostate cases, 27% were brain cases and 39% formed all other cases. In addition, DW-MRI compare to other single imaging procedure formed 53% of all diagnostic procedure that had 87% accurate predictability of prostate and brain cases. It can therefore concluded that DW-MRI is the best single imaging procedure that can be used to diagnose prostate cancers and brain tumors. It has a major advantage of non-ionizing radiation technique, with multiple planes image acquisitions, together with superior soft tissue contrast. In addition its perfusion allow for precise tissue characterization rather than merely 'macroscopic' imaging and superior visualization of both active parts of the brain during certain activities and understanding of the underlying networks. However, there are two outstanding challenges of DW-MRI scans in Ghana: it is expensive as compared to other modalities and not safe for patients with some metal implants. Despite these challenges, its advantages override its disadvantages and therefore it is recommended to clinicians as the first diagnostic tool to use in prostate cancer and brain tumor diagnoses.


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