Abstract
Medical image processing has a very important role in medical diagnosis where a doctor can compare the scanned image of his patient with a heap of images and find the result of the image that matches with it. With the help of feature descriptors, we can make the process of image classification much more efficient. By implementing various feature descriptors, we are able to identify Alzheimer’s at the very early stages which helps the entire curing process faster. This paper presents the comparison of various binary descriptors such as local binary pattern (LBP), local wavelet pattern (LWP), histogram-oriented gradients (HOG), local bit plane decoded pattern (LBDP) along with K-nearest neighbour (KNN) for its classification. The results indicate that the combination of LBP and KNN together produce a better accuracy of 91.21% in “Alzheimer’s Dataset” ( Alzheimer's Dataset (4 class of Images) https://www.kaggle.com/tourist55/alzheimers-dataset-4-class-of-images [1]) when compared to other descriptors.
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References
Alzheimer's Dataset (4 class of Images) https://www.kaggle.com/tourist55/alzheimers-dataset-4-class-of-images
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Nicholas, B., Jayakumar, A., Titus, B., Remya Nair, T. (2022). Comparative Study of Multiple Feature Descriptors for Detecting the Presence of Alzheimer’s Disease. In: Karuppusamy, P., Perikos, I., García Márquez, F.P. (eds) Ubiquitous Intelligent Systems. Smart Innovation, Systems and Technologies, vol 243. Springer, Singapore. https://doi.org/10.1007/978-981-16-3675-2_25
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DOI: https://doi.org/10.1007/978-981-16-3675-2_25
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