A Framework for Classification of Gabor Based Frequency Selective Bone Radiographs Using CNN

Nemati, Rehan J, Riaz, Farhan, Hassan, Ali , Abbas, Muhammad, Rehman, Saad, Hussain, Farhan, Rubab, Saddaf and Azad, Muhammad Ajmal (2021) A Framework for Classification of Gabor Based Frequency Selective Bone Radiographs Using CNN. Arabian Journal for Science and Engineering, 46 (4). pp. 4141-4152. ISSN 2193-567X

Full content URL: https://doi.org/10.1007/s13369-021-05339-7

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Item Type:Article
Item Status:Live Archive


The automatic classification of bone texture into healthy or osteoporotic cases presents a major challenge since there is no visual difference between the two cases. This classification requires an inspection of the fine granularity in the bone radiographs which is usually difficult with a naked eye. We have proposed a novel method in this paper, that can be used for the classification of bone radiographs into healthy or osteoporotic cases. We mimic the observations of the physicians by preprocessing the bone radiographs with Gabor filters bearing a high frequency. Later, we design and utilize a convolutional neural network wherein filtered images are fed as input to the system which classifies the images into their respective classes. The proposed algorithm has been validated on a bone radiograph challenge dataset. Our results depict that the method proposed in this research exhibits very good results in terms of classification. A comparison of the proposed and the contemporary research methods has also been shown in this paper. The experimental results show that by exploiting high frequency Gabor filters and employing the convolutional neural network architecture, good results in performing the classification of bone radiographs are achieved.

Keywords:computer science
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:52380
Deposited On:16 Nov 2022 13:08

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