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Diagnosis of Alzheimer’s Diseases from MRI Images using Image Processing and Machine Learning Approach

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Vandana B.S., Sathyavathi R. Alva

Vandana B.S. and Sathyavathi R Alva. Diagnosis of Alzheimer’s Diseases from MRI Images using Image Processing and Machine Learning Approach. International Journal of Computer Applications 183(26):16-22, September 2021. BibTeX

	author = {Vandana B.S. and Sathyavathi R. Alva},
	title = {Diagnosis of Alzheimer’s Diseases from MRI Images using Image Processing and Machine Learning Approach},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2021},
	volume = {183},
	number = {26},
	month = {Sep},
	year = {2021},
	issn = {0975-8887},
	pages = {16-22},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2021921641},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Alzheimer disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer's disease can help with proper treatment and prevent brain tissue damage. In this work we proposed two methods. Radiological feature extraction using image processing and machine learning from MRI images and Analysis of Alzheimer’sdiseases state by using deep learning approach. In the first phase, the algorithm first normalizes and removes skull from the MRI images. Modified K-Means algorithm is used to partition the image into white matter (WM), grey matter (GM) and black holes (BH). The relevant diagnostic features are extracted from the segmented image component. The classifier is trained by the training data to predict the test data. The features are defined to construct classification model by using Support Vector Machine. In the above techniques, the work was carried out with specific features. Unlike this, deep learning method studies profound features from lower level to higher level without human intervention. Here, database contains total of 1000 images which are resized into 350 × 350 without loss of information. Deep Learning demands large number of images and its strength was increased as per requirement by augmentation technique. In the first phase of the method takes 1000 images of different features are selected to train SVM classifier and the accuracy obtained is 92%. In the second Phase success rate is 85.6%, and contribution of this work is classification of images into categories such as Alzheimer (AD)and normal. First phase of workemphasized program specific applications to extract features.In the second phase the CNN multiple layers which are studied from lower level to the higher-level image characteristics.


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Alzheimer, Classification, Deep-Learning, MRI images, Segmentation