## 4 Performance Analysis  
All the trials were conducted by using the GforceGTX 770 and CUDNN 5110 graphics cards, together with Python 2.7 and the Spyder Integrated Development Environment (IDE). Using a standard dataset and a learning rate of 0.5, the performance of the proposed watermarking framework was evaluated. Various epochs were used to train our model. **Dataset description:** In this study, two separate datasets were used. The first one was obtained between 2005 and 2010 from Nanfang Hospital and General Hospital at Tianjin Medical University in China. It was then published online in a number of editions beginning in 2015 and ending with its final release in 2017. Axial, coronal, and sagittal images are all included in the dataset. The collection contains T1-weighted contrast-enhanced pictures from 233 patients with meningioma, glioma, and pituitary tumours, which are three different forms of brain cancers. Depending on their types and grades, brain tumours can vary in size, location, and shape. The Cancer Imaging Archive (TCIA), a public access resource, provided a second dataset. In this dataset, there are 130 patients with various diseases, grades, races, and ages, whose MRI multi-sequence pictures can be found in the Repository of Molecular Brain Neoplasia Data (REMBRANDT). T1-weighted contrast-enhanced images that contained gliomas of various grades were selected. Additionally, the Free Mammogram Database patient data were searched for mammograms (MIAS). Ten mammograms were additionally used to train the classifier. The 46 mammograms comprise 10 benign and 36 malignant masses that are in thick glandular tissue locations, ducts, breast boundaries, blood vessels, and/or glandular tissues in various breast places. Table 1 shows a comparative analysis between the proposed and existing techniques based on various medical images. Here, the used datasets are TCIA, REMBRANDT, and MIAS datasets. The parameters analysed are bit error rate, SSIM, NCC, training accuracy, and validation accuracy. Here the training and testing have been carried out based on a number of epochs. The above Figure 6a-e shows a comparative analysis between the proposed and existing techniques for the TCIA dataset. The proposed technique attained a bit error rate of 59%, an SSIM of 45%, an NCC of 56%, a training accuracy of 89%, and a validation accuracy of 85%. ASMA attained a bit error rate of 55%, an SSIM of 42%, an NCC of 52%, a training accuracy of 85%, and a validation accuracy of 81%. Regarding DDWT, it attained a bit error rate of 56%, an SSIM of 43%, a Normalized Cross-Correlation of 54%, a training accuracy of 88%, and a validation accuracy of 83%. Figure 7a-e shows a comparative analysis between the proposed and existing technique for the REMBRANDT dataset. The proposed technique attained a bit error rate of 65%, an SSIM of 55%, a Normalized Cross-Correlation of 63%, a training accuracy of 95%, and a validation accuracy of 89%. Additionally, ASMA attained a bit error rate of 61%, an SSIM of 51%, a Normalized Cross-Correlation of 58%, a training accuracy of 92%, and a validation accuracy of 82%. In the case of DDWT, it attained a bit error rate of 63%, an SSIM of 53%, a Normalized Cross-Correlation of 61%, a training accuracy of 93%, and a validation accuracy of 85%. Figure 8a-e shows a comparative analysis between the proposed and existing techniques for the MIAS dataset. The proposed technique attained a bit error rate of 71%, an SSIM of 56%, a Normalized Cross-Correlation of 71%, a training accuracy of 98%, and a validation accuracy of 95%. Regarding ASMA, this attained a bit error rate of 67%, an SSIM of 52%, a Normalized Cross-Correlation of 65%, a training accuracy of 94%, and a validation accuracy of 91%. DDWT attained a bit error rate of 68%, an SSIM of 54%, a Normalized Cross-Correlation of 68%, a training accuracy of 96%, and a validation accuracy of 93%.