Archana Harsing Sable

Summary: Breast cancer is the most prevalent kind of tumor to occur in females and the primary cause of death for women. Early detection is perhaps the most successful strategy to minimize breast cancer mortality. Early diagnosis necessitates a consistent and efficient diagnostics method that allows doctors to differentiate benign from malignant breast cancers without a surgical sample. The goal of this endeavor is to develop a sophisticated breast cancer diagnosis method. The primary goal of the paper is to reduce the death rate among women by promoting early detection of breast cancer. First, pre‐processing techniques such as median filtering and contrast limiting adaptive histogram equalization are used to the obtained raw images. By doing this, the machine‐learning model's computational complexity is decreased and the image quality is enhanced. K‐means clustering is used to segregate the pre‐processed image. Additionally, features including the enhanced local vector pattern, grey‐level co‐occurrence matrix and local vector patterns are produced in the course of the feature extraction stage. Finally, an optimized deep belief network (DBN) is carrying out the classification process. To boosts the classification accuracy, activation function of DBN (tanh, softmax, ReLu) as well as its weight function is optimized by the proposed grey wolf updated whale optimization algorithm The accuracy of the greywolf updated whale optimization algorithm+DBN is above 93% for datasets 1 and 2 when compared to extant models. Finally, calculation of the performance validates the proposed model's performance.

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 chapter presents a new technique called entropy volume-based scale-invariant feature transform for correct face recognition post cosmetic surgery. The comparable features taken are the key points and volume of the Difference of Gaussian (DOG) structure for those points the information rate is confirmed. The information extracted has a minimum effect on uncertain changes in the face since the entropy is the higher-order statistical feature. Then the extracted corresponding entropy volume-based scale-invariant feature transform features are applied and provided to the support vector machine for classification. The normal scale-invariant feature transform feature extracts the key points based on dissimilarity which is also known as the contrast of the image, and the volume-based scale-invariant feature transform (V-SIFT) feature extracts the key points based on the volume of the structure. However, the EV-SIFT method provides both the contrast and volume information. Thus, EV-SIFT provides better performance when compared with principal component analysis (PCA), normal scale-invariant feature transform (SIFT), and V-SIFT-based feature extraction. Since it is well known that the artificial neural network (ANN) with Levenberg-Marquardt (LM) is a powerful computation tool for accurate classification, it is further used in this technique for better classification results.

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...

Automatic recognition of people faces many challenging problems which has experienced much attention due to many applications in different fields during recent years. Face recognition is one of those challenging problem which does not have much technique to solve all situations like pose, expression, and illumination changes, and/or ageing. Facial expression due to plastic surgery is one of the additional challenges which arise recently. This paper presents a new technique for accurate face recognition after the plastic surgery. This technique uses Entropy based SIFT (EV-SIFT) features for the recognition purpose. The corresponding feature extracts the key points and volume of the scale-space structure for which the information rate is determined. This provides least effect on uncertain variations in the face since the entropy is the higher order statistical feature. The corresponding EV-SIFT features are applied to the Support vector machine for classification. The normal SIFT feature extracts the key points based on the contrast of the image and the V- SIFT feature extracts the key points based on the volume of the structure. But the EV- SIFT method provides the contrast and volume information. This technique provides better performance when compare with PCA, normal SIFT and V-SIFT based feature extraction.

Today, the research in face recognition has focused on developing algorithms that have been proposed to recognize faces beyond variations in viewpoint, illumination, pose and expression. In this paper we have proposed face recognition algorithms, which works in two phase frequency domain i.e., TPFR-DCT-Mah, TPFR-DFT-Mah and TPFR-DWT-Mah. For TPFR-DWT-Mah, the low-frequency subband coefficients i.e. LL subbands (after two-level wavelet decomposition) are used as DWT coefficients, TPFR-DCT-Mah uses the absolute values of DCT coefficients and TPFR-DFT-Mah uses DFT amplitude spectrums to represent the face image, i.e. the transformed image. The first step of the proposed algorithm seeks to represent the test sample as a transformed image and exploits the Mahalanobis distance of each training sample with the test sample to determine K "nearest neighbors" for the test sample. The second step represents the test sample as a linear combination of the determined K nearest neighbors and uses the representation result to perform classification. And finally classify the test sample into the class that has minimum deviation. The proposed algorithm assumes that the K "nearest neighbors" are from the same class as the test sample. The accuracy of the proposed algorithms i.e., TPFR-DCT-Mah, TPFR-DFT-Mah and TPFR-DWT-Mah has been identified and a comparison was performed between them in terms of recognition rates or Equal Error Rate(EER) or the Receiver Operating Curves(ROC).

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.