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Electrocardiogram (ECG) watermarking provides secure communication of patient information lies in a 1D- ECG signal. The primary challenge in ECG watermarking is the deterioration of an ECG signal which causes the loss and impotence to extract patient information. This paper proposes a wavelet method based watermarking scheme for patient information hiding in the ECG as a QR image. Here, we first convert the 1D-ECG signal to 2D-ECG image using the Pan–Tompkins algorithm. We use a wavelet transform to decompose 2D-ECG image. Wavelet analysis can capture the subtle underlying information of the ECG. Then we further decompose the detail coefficient of wavelet and the QR image using QR decomposition for embedding data. The embedding factor value calculation is adaptive by harnessing the entropy value of the signal. The hidden data is easily extractable with no distortion at the extractor side. The ECG data we use in this paper is from the MIT-BIH database. The results on this dataset suggest that our proposed approach is useful in patient information data hiding scheme in ECG. The proposed method outperforms the state-of-the-art.

Chest X-Ray (CXR) images provide most anatomical details and the abnormalities on a 2D plane. Therefore, a 2D view of the 3D anatomy is sometimes sufficient for the initial diagnosis. However, close to fourteen commonly occurring diseases are sometimes difficult to identify by visually inspecting the images. Therefore, there is a drift toward developing computer-aided assistive systems to help radiologists. This paper proposes a deep learning model for the classification and localization of chest diseases by using image-level annotations. The model consists of a modified Resnet50 backbone for extracting feature corpus from the images, a classifier, and a pixel correlation module (PCM). During PCM training, the network is a weight-shared siamese architecture where the first branch applies the affine transform to the image before feeding to the network, while the second applies the same transform to the network output. The method was evaluated on CXR from the clinical center in the ratio of 70:20 for training and testing. The model was developed and tested using the cloud computing platform Google Colaboratory (NVidia Tesla P100 GPU, 16 GB of RAM). A radiologist subjectively validated the results. Our model trained with the configurations mentioned in this paper outperformed benchmark results.

1. Wheat, triticale 131 or triticale 72-S could replace up to half the maize in broiler diets without affecting performance. Replacing more of the maize on either an equal nitrogen basis or a weight basis adversely affected performance.2. Triticale 72-S appeared to be superior to triticale 131 as a direct replacement for maize, but both grains were inferior to maize as energy sources.