Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments

This paper proposes a new blind, color image watermarking method using fast Walsh–Hadamard transformation (FWHT) and multi-channel fractional Legendre–Fourier moments (MFrLFMs). The input host color image is first split into 4 × 4 non-interfering blocks, and the MFrLFMs are computed for each block, where proper MFrLFMs coefficients are selected and FWHT is applied on the selected coefficients. The scrambled binary watermark has been inserted in the quantized selected MFrLFMs coefficients. The proposed method is a blind extraction, as the original host image is not required to extract the watermark. The proposed method is evaluated over many visual imperceptibility terms such as peak signal-to-noise ratio (PSNR), normalized correlation (NC), and bit error rate. The robustness of the proposed method is tested over several geometrical attacks such as scaling, rotation, cropping, and translation with different parameter values. The most widely recognized image processing attacks are also considered, e.g., compressing and adding noise attacks. A set of combination attacks are also tested to analyze the robustness of the proposed scheme versus several attacks. The proposed model’s experimental and numerical results for invisibility and robustness were superior to the results of similar watermarking methods.

DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity

Retinopathy of Prematurity (ROP) affects preterm neonates and could cause blindness. Deep learning (DL) can assist ophthalmologists in the diagnosis of ROP. This paper proposes an automated and reliable diagnostic tool based on DL techniques called DIAROP to support the ophthalmologic diagnosis of ROP. It extracts significant features by first obtaining spatial features from the four convolution neural networks (CNNs) DL techniques using transfer learning and then applying Fast Walsh Hadamard Transform (FWHT) to integrate these features. Moreover, DIAROP explores the best-integrated features extracted from the CNNs that influence its diagnostic capability. The results of DIAROP indicate that DIAROP achieved an accuracy of 93.2% and an area under receiving operating characteristic curve (AUC) of 0.98. Furthermore, DIAROP performance is compared with recent ROP diagnostic tools. Its promising performance shows that DIAROP may assist the ophthalmologic diagnosis of ROP.

Stress Detection using EEG Signal Based on Fast Walsh Hadamard transform and Voting Classifier

Mental stress is currently a significant concern, especially among the young. Stress adversely affects the overall performance of people’s work, and in certain cases, can even cause serious health issues. Everyone experiences stress in life. A unique way to identify and classify stress levels based on Electroencephalogram (EEG) is proposed in this manuscript. In this work, fast Walsh Hadamard transform is used to generate all frequencies which exist in the EEG signals. The range of alpha, beta, gamma, and delta from index value is calculated in subsequent stage. Principal component analysis (PCA) is applied for the feature dimensional reduction which is followed by the standard scaler. The PSD vector has been calculated for healthy and unhealthy EEG signal groups using the Welch method. The PSD vector is used an input to the voting classifier which is the combination of the k-NN and logistic regression classifier. The experimental results found that the proposed method provides better results when compared to the existing methods in terms of Accuracy (Acc) and Mean Square Error (MSE). The proposed method achieves a highest classification accuracy of 94.22%