Early detection of breast cancer is an essential procedure to reduce the mortality rate among women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called ETECADx is proposed by fusing the benefits of both ensemble transfer learning of the convolutional neural networks as well as the self-attention mechanism of vision transformer encoder (ViT). The accurate and precious high-level deep features are generated via the backbone ensemble network, while the transformer encoder is used to diagnose the breast cancer probabilities in two approaches: Approach A (i.e., binary classification) and Approach B (i.e., multi-classification). To build the proposed CAD system, the benchmark public multi-class INbreast dataset is used. Meanwhile, private real breast cancer images are collected and annotated by expert radiologists to validate the prediction performance of the proposed ETECADx framework. The promising evaluation results are achieved using the INbreast mammograms with overall accuracies of 98.58% and 97.87% for the binary and multi-class approaches, respectively. Compared with the individual backbone networks, the proposed ensemble learning model improves the breast cancer prediction performance by 6.6% for binary and 4.6% for multi-class approaches. The proposed hybrid ETECADx shows further prediction improvement when the ViT-based ensemble backbone network is used by 8.1% and 6.2% for binary and multi-class diagnosis, respectively. For validation purposes using the real breast images, the proposed CAD system provides encouraging prediction accuracies of 97.16% for binary and 89.40% for multi-class approaches. The ETECADx has a capability to predict the breast lesions for a single mammogram in an average of 0.048 s. Such promising performance could be useful and helpful to assist the practical CAD framework applications providing a second supporting opinion of distinguishing various breast cancer malignancies.

This paper propose a double bilateral filter that extends the classical bilateral filtering for image noise removal. A new median-metric weighting function is introduced by incorporating a 3 × 3 median filter into a second bilateral filter. A variety of images contaminated by various degrees of Gaussian, impulse, and mixed noise were used to assess the performance of this new filtering method. It is indicated that the double bilateral filter outperformed several existing methods in both visual image quality and restored signal quantity. In this paper, we propose a Modified double bilateral filter where we replaced 3 × 3median filter with a new decision-based algorithm based median filter in the second bilateral filter. The proposed method, unlike other nonlinear filters, removes only corrupted pixel by the median value or by its neighboring pixel value. As a result of this, the proposed method removes the noise effectively even at noise level as high as 90% and preserves the edges without any loss up to 80% of noise level. The new proposed median filter is for restoration of images that are highly corrupted by impulse noise.

Face recognition is one of the challenging problems which suffer from practical issues like the pose, expression, illumination changes, and aging. Plastic surgery is one among the issues that pose...

Today, much research on face recognition has focused on using grey-scale images. With the increasing availability of color images, it makes sense to develop approaches for integrating color information into recognition process as the grey-scale approaches is sensitive to lighting variations. In this paper, we have proposed a novel two phase method, i.e., YCbCr-WPD-PCA-Mah. In the first phase, we convert the each training face into Y, Cb and Cr components and then decompose Y, Cb and Cr components into k parts using the Wavelet Packet Decomposition(WPD). finally perform PCA for k-times on Y, Cb and Cr subbands, to get k eigenspaces and k feature vectors for each Y, Cb and Cr subbands. In the second phase i.e., classification phase, the test image is projected onto the Y-eigenspace, Cb-eigenspace, and Cr-eigenspace after being decomposed into k part using WPD. Then, the Mahalanobis Distance is computed between the test image and all the training images in Y-subspace, Cb-subspace, and Cr-subspace. The Mahalanobis Distance is computed between the merged feature vectors. In deceision level, we compute the mean of the Mahalanobis Distances obtained from Y, Cb and Cr subspaces. The face has the best match with the test image is which has the minimum distance. The accuracy of the proposed method i.e., YCbCr-WPD-PCA-Mah has been identified and a comparison was performed in terms of recognition rates or Equal Error Rate(EER) or the Receiver Operating Curves(ROC)