Authors:
Fred N. Kiwanuka
1
;
Omar Eltaher Abuelmaatti
1
;
Anang Hudaya Muhamad Amin
1
and
Brian J. Mukwaya
2
Affiliations:
1
Division of Computer and Information Science, Higher Colleges of Technology, Dubai, U.A.E.
;
2
PredictX, Uganda
Keyword(s):
Max Tree, Connected Operators, Connected Filters, Attribute Filters, Skin Diseases, Deep Learning.
Abstract:
Morphological connected filters operate on an image through flat zones which comprise the largest connected components with a constant signal. These filters identify and ultimately extract the whole connected components in an image without alteration of their boundaries and thus shape preserving. This is a desirable property in many image processing and analysis applications. However, due to the variability of the number of connected components, even in the case of images of the same resolution and size, their application in classification tasks has been limited. In this study, we propose an approach that computes the shape and size features of connected components and use these features for the classification of bacterial and viral tropical skin infections. We demonstrate the performance of the approach using gradient boosting machines and compare the results to deep learning approaches. Results show that the performance of our approach is comparable to that of Convolutional Neural
Networks (CNN) based approach when trained on 1460 images. Moreover, CNN was pre-trained and required augmentation to achieve that perfomance. However, our approach is at least 56% faster than CNN.
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