The mobile networks are rapidly evolving, transitioning from specialized hardware to fully virtualized platforms that run on commercial off-the-shelf (COTS) hardware powered by general-purpose processors. Mobile opera...
详细信息
Breast cancer poses a threat to women’s health and contributes to an increase in mortality rates. Mammography has proven to be an effective tool for the early detection of breast cancer. However, it faces many challe...
详细信息
Breast cancer poses a threat to women’s health and contributes to an increase in mortality rates. Mammography has proven to be an effective tool for the early detection of breast cancer. However, it faces many challenges in early breast cancer detection due to poor image quality, traditional segmentation, and feature extraction. Therefore, this work addresses these issues and proposes an attention-based backpropagation convolutional neural network (ABB-CNN) to detect breast cancer from mammogram images more accurately. The proposed work includes image enhancement, reinforcement learning-based semantic segmentation (RLSS), and multiview feature extraction and classification. The image enhancement is performed by removing noise and artefacts through a hybrid filter (HF), image scaling through a pixel-based bilinear interpolation (PBI), and contrast enhancement through an election-based optimization (EO) algorithm. In addition, the RLSS introduces intelligent segmentation by utilizing a deep Q network (DQN) to segment the region of interest (ROI) strategically. Moreover, the proposed ABB-CNN facilitates multiview feature extraction from the segmented region to classify the mammograms into normal, malignant, and benign classes. The proposed framework is evaluated on the collected and the digital database for screening mammography (DDSM) datasets. The proposed framework provides better outcomes in terms of accuracy, sensitivity, specificity, precision, f-measure, false-negative rate (FNR) and area under the curve (AUC). This work achieved (99.20%, 99.35%), (99.56%, 99.66%), (98.96%, 98.99%), (99.05%, 99.12%), (0.44%, 0.34%), (99.31%, 99.39%) and (99.27%, 99.32%) of accuracy, sensitivity, specificity, precision, FNR, f-measure and AUC on (collected, DDSM datasets), respectively. This research addresses the prevalent challenges in breast cancer identification and offers a robust and highly accurate solution by integrating advanced deep-learning techniques. The evaluated re
There is a need for tagging of Multimedia content on the Web, specifically images, in addition, most of these images are from the medical domain continue to be neglected over the Web 3.0 and they need to be annotated ...
详细信息
With the enhanced usage of artificial-intelligence-driven applications, the researchers often face challenges in improving the accuracy of data classification models, while trading off the complexity. In this article,...
详细信息
IoT healthcare security is increasingly important, as the interconnectedness of medical devices in itself introduces a major vulnerability, given the impact on patient safety and data integrity. Past works in this dom...
详细信息
This article provides a vision of combining Wireless Isochronous Real Time (WIRT) in-X Subnetworks with the Information Centric Networking framework. Here, the advantages of ICN over traditional IP-based networks are ...
详细信息
Serverless computing has become the buzzword in IT and it has been widely accepted because of its diverse cloud computing benefits. One of the major drawbacks of serverless computing is the security of the data which ...
详细信息
The domain of natural language processing has reached to a level where various tools are available for automatically identifying and correcting grammatical errors in a wide range of languages text. Though these tools ...
详细信息
The product review gives critical data for both businesses and consumers, offering insights needed before buying a service or product. However, the existing methods has drawback of there is not understanding semantic ...
详细信息
Forest fires, a dangerous natural phenomenon, cause large-scale destruction in forests and nearby communities. In this paper, we leverage the capabilities of classification and fast prediction of machine learning and ...
详细信息
暂无评论