# A Novel Fragmented Approach for Securing Medical Health Records in Multimodal Medical Images
## 4 Results and Discussion
Figure 6 shows the SSIM values for 21 patients' images hidden with perception with various character sizes. It is observed that when running the experiment for multiple patients and various character sizes of prescription, the SSIM values were almost 1, indicating that the original image and the stego image are almost identical. With medical images being used for medical diagnosis, the fear is that the hiding of information and altering the image \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{2}{c}{**Medical Records**} & \multicolumn{1}{c}{**Avg. PSNR**} & \multicolumn{1}{c}{**Avg. MSE**} & \multicolumn{1}{c}{**Avg. SSIM**} \\ \hline Text 1 (100 characters) & 59.569 & 0.1299 & 0.9998 & 0.9999 \\ Text 2 (600 characters) & 58.689 & 0.1271 & 0.9997 & 0.9999 \\ Text 3 (1200 characters) & 58.481 & 0.1307 & 0.9998 & 0.9999 \\ Text 4 (1800 characters) & 57.853 & 0.0204 & 0.9999 & 1 \\ Text 5 (3000 characters) & 57.170 & 0.0549 & 0.9998 & 0.9999 \\ Text 6 (5000 characters) & 57.019 & 0.1509 & 0.9997 & 0.9999 \\ \hline \hline \end{tabular}
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Table 4: SNR- and PSNR-based comparison for the proposed approach with different patient prescription text samples hidden in the medical images with LSB offset value 1. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Prescriptions**} & \multirow{2}{*}{**Text Size**} & \multirow{2}{*}{**No. of LSB Bits**} & \multicolumn{1}{c}{**Max Capacity**} & \multirow{2}{*}{**Avg. SSIM**} & \multirow{2}{*}{**Avg. SSIM**} \\  & & & & **(Bits)** & & \\ \hline \multirow{4}{*}{Text 1} & \multirow{4}{*}{800} & 1 & 57,600 & 59.31 & 59.08 & 59.49 \\  & & 2 & 115,200 & 56.86 & 56.88 & 57.00 \\  & & 3 & 172,800 & 53.1 & 53.31 & 53.45 \\  & & 4 & 230,400 & 51.56 & 51.53 & 51.61 \\ \hline \multirow{4}{*}{Text 2} & \multirow{4}{*}{4800} & 1 & 57,600 & 58.6 & 58.58 & 58.62 \\  & & 2 & 115,200 & 56.51 & 56.54 & 56.57 \\  & & 3 & 172,800 & 53.2 & 53.01 & 53.10 \\  & & 4 & 230,400 & 51.49 & 51.48 & 51.49 \\ \hline \multirow{4}{*}{Text 3} & \multirow{4}{*}{9600} & 1 & 57,600 & 58.44 & 58.37 & 58.45 \\  & & 2 & 115,200 & 56.45 & 56.29 & 56.44 \\  & & 3 & 172,800 & 53.02 & 52.61 & 53.04 \\  & & 4 & 230,400 & 51.45 & 51.40 & 51.43 \\ \hline \multirow{4}{*}{Text 4} & \multirow{4}{*}{14,400} & 1 & 57,600 & 58.06 & 57.9 & 58.24 \\  & & 2 & 115,200 & 56.12 & 56.04 & 56.28 \\  & & 3 & 172,800 & 52.42 & 52.46 & 52.44 \\  & & 4 & 230,400 & 51.41 & 51.37 & 51.40 \\ \hline \multirow{4}{*}{Text 5} & \multirow{4}{*}{24,000} & 1 & 57,600 & 57.82 & 57.80 & 57.82 \\  & & 2 & 115,200 & 55.63 & 55.95 & 55.98 \\  & & 3 & 172,800 & 52.39 & 52.35 & 52.41 \\  & & 4 & 230,400 & 51.22 & 51.39 & 51.34 \\ \hline \multirow{4}{*}{Text 6} & \multirow{4}{*}{40,000} & 1 & 57,600 & 57.56 & 56.39 & 56.56 \\  & & 2 & 115,200 & 55.42 & 53.24 & 53.85 \\ \cline{1-1}  & & 3 & 172,800 & 52.30 & 51.46 & 51.94 \\ \cline{1-1}  & & 4 & 230,400 & 51.32 & 50.39 & 50.50 \\ \hline \hline \end{tabular}
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Table 3: Proposed method comparison of sample MR images PSNR using different prescription text sizes and LSB bits tested on sample MR images. may somehow affect the quality of the image for diagnosis. Nowadays, research is being performed on using deep learning models to aid physicians in the automatic diagnosis of medical conditions based on medical images. The ultimate test of a proposed steganography method is to prove that the image quality will not jeopardize the diagnosis process for patients, which is a serious matter. For this purpose, we used a Residual Network (ResNet) Convolutional Neural Network (CNN) model proposed in [36] for the automatic detection of brain tumors. We executed this approach on the original and stego images. If the results are excellent, then this would indicate the stego image is highly similar to the original image and will not affect the diagnosis process. Several metrics, such as accuracy, precision, recall, and F1-measure, are used to measure classification fidelity.