Matches in SemOpenAlex for { <https://semopenalex.org/work/W3126266103> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W3126266103 abstract "Brain atrophy is the degradation of brain cells and tissues to the extent that it is clearly indicative during Mini-Mental State Exam test and other psychological analysis. It is an alarming state of the human brain that progressively results in Alzheimer disease which is not curable. But timely detection of brain atrophy can help millions of people before they reach the state of Alzheimer. In this study we analyzed the longitudinal structural MRI of older adults in the age group of 42 to 96 of OASIS 3 Open Access Database. The nth slice of one subject does not match with the nth slice of another subject because the head position under the magnetic field is not synchronized. As a radiologist analyzes the MRI image data slice wise so our system also compares the MRI images slice wise, we deduced a method of slice by slice registration by driving mid slice location in each MRI image so that slices from different MRI images can be compared with least error. Machine learning is the technique which helps to exploit the information available in abundance of data and it can detect patterns in data which can give indication and detection of particular events and states. Each slice of MRI analyzed using simple statistical determinants and Gray level Co-Occurrence Matrix based statistical texture features from whole brain MRI images. The study explored varied classifiers Support Vector Machine, Random Forest, K-nearest neighbor, Naive Bayes, AdaBoost and Bagging Classifier methods to predict how normal brain atrophy differs from brain atrophy causing cognitive impairment. Different hyper parameters of classifiers tuned to get the best results. The study indicates Support Vector Machine and AdaBoost the most promising classifier to be used for automatic medical image analysis and early detection of brain diseases. The AdaBoost gives accuracy of 96.76% with specificity 95.87% and sensitivity 87.37% and receiving operating curve accuracy 96.3%. The SVM gives accuracy of 96% with 92% specificity and 87% sensitivity and receiving operating curve accuracy 95.05%." @default.
- W3126266103 created "2021-02-15" @default.
- W3126266103 creator A5028592258 @default.
- W3126266103 creator A5046611555 @default.
- W3126266103 creator A5069598330 @default.
- W3126266103 creator A5088989354 @default.
- W3126266103 date "2021-01-01" @default.
- W3126266103 modified "2023-10-06" @default.
- W3126266103 title "Amalgamation of Machine Learning and Slice-by-Slice Registration of MRI for Early Prognosis of Cognitive Decline" @default.
- W3126266103 cites W1965429322 @default.
- W3126266103 cites W1973637437 @default.
- W3126266103 cites W1979062697 @default.
- W3126266103 cites W1980390607 @default.
- W3126266103 cites W1984855952 @default.
- W3126266103 cites W1997194251 @default.
- W3126266103 cites W2004222997 @default.
- W3126266103 cites W2007506293 @default.
- W3126266103 cites W2016740629 @default.
- W3126266103 cites W2028580299 @default.
- W3126266103 cites W2052742260 @default.
- W3126266103 cites W2081320913 @default.
- W3126266103 cites W2098017711 @default.
- W3126266103 cites W2142185053 @default.
- W3126266103 cites W2148601182 @default.
- W3126266103 cites W2164870728 @default.
- W3126266103 cites W2765979958 @default.
- W3126266103 cites W2769556951 @default.
- W3126266103 cites W2789337348 @default.
- W3126266103 cites W2886872798 @default.
- W3126266103 cites W2944950225 @default.
- W3126266103 cites W2995673771 @default.
- W3126266103 cites W3005713147 @default.
- W3126266103 cites W3006901343 @default.
- W3126266103 doi "https://doi.org/10.14569/ijacsa.2021.0120115" @default.
- W3126266103 hasPublicationYear "2021" @default.
- W3126266103 type Work @default.
- W3126266103 sameAs 3126266103 @default.
- W3126266103 citedByCount "2" @default.
- W3126266103 countsByYear W31262661032022 @default.
- W3126266103 crossrefType "journal-article" @default.
- W3126266103 hasAuthorship W3126266103A5028592258 @default.
- W3126266103 hasAuthorship W3126266103A5046611555 @default.
- W3126266103 hasAuthorship W3126266103A5069598330 @default.
- W3126266103 hasAuthorship W3126266103A5088989354 @default.
- W3126266103 hasBestOaLocation W31262661031 @default.
- W3126266103 hasConcept C119857082 @default.
- W3126266103 hasConcept C12267149 @default.
- W3126266103 hasConcept C141404830 @default.
- W3126266103 hasConcept C153180895 @default.
- W3126266103 hasConcept C154945302 @default.
- W3126266103 hasConcept C169258074 @default.
- W3126266103 hasConcept C41008148 @default.
- W3126266103 hasConcept C52001869 @default.
- W3126266103 hasConceptScore W3126266103C119857082 @default.
- W3126266103 hasConceptScore W3126266103C12267149 @default.
- W3126266103 hasConceptScore W3126266103C141404830 @default.
- W3126266103 hasConceptScore W3126266103C153180895 @default.
- W3126266103 hasConceptScore W3126266103C154945302 @default.
- W3126266103 hasConceptScore W3126266103C169258074 @default.
- W3126266103 hasConceptScore W3126266103C41008148 @default.
- W3126266103 hasConceptScore W3126266103C52001869 @default.
- W3126266103 hasIssue "1" @default.
- W3126266103 hasLocation W31262661031 @default.
- W3126266103 hasOpenAccess W3126266103 @default.
- W3126266103 hasPrimaryLocation W31262661031 @default.
- W3126266103 hasRelatedWork W1996541855 @default.
- W3126266103 hasRelatedWork W2509892558 @default.
- W3126266103 hasRelatedWork W2904660175 @default.
- W3126266103 hasRelatedWork W2911792412 @default.
- W3126266103 hasRelatedWork W2985924212 @default.
- W3126266103 hasRelatedWork W3204641204 @default.
- W3126266103 hasRelatedWork W4375930479 @default.
- W3126266103 hasRelatedWork W4377964522 @default.
- W3126266103 hasRelatedWork W4381414210 @default.
- W3126266103 hasRelatedWork W4386072274 @default.
- W3126266103 hasVolume "12" @default.
- W3126266103 isParatext "false" @default.
- W3126266103 isRetracted "false" @default.
- W3126266103 magId "3126266103" @default.
- W3126266103 workType "article" @default.