Dongxi Liu4 Author

Research Article Open Access

Evaluation of the Effect of Steganography on Medical Image Classification Accuracy

Author(s):

Peter Eze1*, Udaya Parampalli2, Robin Evans3 and Dongxi Liu4

Predictors and features that are used in teleradiology and machinebased auto diagnosis in medicine are often not put into consideration while evaluating medical image Steganography algorithms. In this paper, the effect of embedded security data in automated diagnosis was evaluated using Support Vector Machine (SVM) image classification of Chest X-rays Scan of Normal and Pneumonia patients. The goal is to quantify and qualify disease classification parameters because of the addition of steganographic security data in to the image. Four textural image features: Contrast,Homogeneity Energy, and Entropy were used as medical image biomarkers. Their statistical properties for the disease conditions (normal or pneumonia)were profiled and used in SVM training. The evaluation parameters for the ... view more»

DOI: 10.37532/2329-9533.2020.9(4).176

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