%0 Journal Article %1 Sinha_2015 %A Sinha, Swapnil %A Kushwaha, Sunil Kumar %A Burje, Prof. Shrikant B. %A Jain, Shawetangi Kala %A Singh, Akshay %D 2015 %I Auricle Technologies, Pvt., Ltd. %J International Journal on Recent and Innovation Trends in Computing and Communication %K ANN Computing DWT MRI PCA SVM Soft %N 3 %P 934--939 %R 10.17762/ijritcc2321-8169.150308 %T Detection of Brain Injury Using Different Soft Computing Techniques A Survey %U http://dx.doi.org/10.17762/ijritcc2321-8169.150308 %V 3 %X The detection of brain injury is one of the important and difficult task in the field of medicine. If the brain injuries are not detected in time, then it can cause serious problems in patients and sometimes can even lead to death. Traumatic brain injury (TBI) is one of the major causes of mortality and poor quality of life among the survivors. Various imaging techniques are available for taking the images of the brain so that the injuries can be detected. Magnetic resonance imaging (MRI) is one of the common medical imaging technique used for the delineation of soft tissues such as that of the brain. This paper analyses few of the methods and their performances that have been proposed for the detection of the brain injury. In these methods different soft computing techniques such as artificial neural networks (ANN), k nearest neighbor (k-NN), support vector machine (SVM), Parzan window, etc. were used for the classification of abnormal and normal brain images. Before classification feature extraction and reduction were done using the methods such as DWT, GLCM, PCA, etc.

@article{Sinha_2015, abstract = {The detection of brain injury is one of the important and difficult task in the field of medicine. If the brain injuries are not detected in time, then it can cause serious problems in patients and sometimes can even lead to death. Traumatic brain injury (TBI) is one of the major causes of mortality and poor quality of life among the survivors. Various imaging techniques are available for taking the images of the brain so that the injuries can be detected. Magnetic resonance imaging (MRI) is one of the common medical imaging technique used for the delineation of soft tissues such as that of the brain. This paper analyses few of the methods and their performances that have been proposed for the detection of the brain injury. In these methods different soft computing techniques such as artificial neural networks (ANN), k nearest neighbor (k-NN), support vector machine (SVM), Parzan window, etc. were used for the classification of abnormal and normal brain images. Before classification feature extraction and reduction were done using the methods such as DWT, GLCM, PCA, etc.}, added-at = {2015-08-06T07:32:31.000+0200}, author = {Sinha, Swapnil and Kushwaha, Sunil Kumar and Burje, Prof. Shrikant B. and Jain, Shawetangi Kala and Singh, Akshay}, biburl = {https://www.bibsonomy.org/bibtex/268da1dca73b7c1b8f6423269e3d886ac/ijritcc}, doi = {10.17762/ijritcc2321-8169.150308}, interhash = {02b161c458591b35b8e890d69d6dbb50}, intrahash = {68da1dca73b7c1b8f6423269e3d886ac}, journal = {International Journal on Recent and Innovation Trends in Computing and Communication}, keywords = {ANN Computing DWT MRI PCA SVM Soft}, month = {march}, number = 3, pages = {934--939}, publisher = {Auricle Technologies, Pvt., Ltd.}, timestamp = {2015-08-06T07:32:31.000+0200}, title = {Detection of Brain Injury Using Different Soft Computing Techniques A Survey}, url = {http://dx.doi.org/10.17762/ijritcc2321-8169.150308}, volume = 3, year = 2015 }