Chest X-Ray Image Classification on Common Thorax Diseases using GLCM and AlexNet Deep Features


  • Tengku Afiah Mardhiah Tengku Zainul Akmal Universiti Teknologi Malaysia
  • Joel Chia Ming Than Universiti Teknologi Malaysia
  • Haslailee Abdullah Universiti Teknologi Malaysia
  • Norliza Mohd Noor Universiti Teknologi Malaysia


Alexnet, classification, ensemble method, GLCM, supervised classification


Image processing has been progressing far in medical as it is one of the main techniques used in the development of medical imaging diagnosis system. Some of the medical imaging modalities are the Magnetic Resonance Imaging (MRI), Computed Tomography (CT) Scan, X-Ray and Ultrasound. The output from all of these modalities would later be reviewed by the expert for an accurate result. Ensemble methods in machine learning are able to provide an automatic detection that can be used in the development of computer aided diagnosis system which can aid the experts in making their diagnosis. This paper presents the investigation on the classification of fourteen thorax diseases using chest x-ray image from ChestX-Ray8 database using Grey Level Co-occurrence Matrix (GLCM) and AlexNet feature extraction which are process using supervised classifiers: Zero R, k-NN, Naïve Bayes, PART, and J48 Tree. The classification accuracy result indicates that k-NN classifier gave the highest accuracy compare to the other classifiers with 47.51% accuracy for GLCM feature extraction method and 47.18% for AlexNet feature extraction method. The result shows that number of data by class and multilabelled data will influence the classifcation method. Data using GLCM feature extraction method has higher classification accuracy compared to AlexNet and required less processing step.


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How to Cite

Tengku Zainul Akmal, T. A. M., Ming Than, J. C., Abdullah, H., & Mohd Noor, N. (2019). Chest X-Ray Image Classification on Common Thorax Diseases using GLCM and AlexNet Deep Features. International Journal of Integrated Engineering, 11(4). Retrieved from

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