An innovative breast cancer detection framework using multiscale dilated densenet with attention mechanism

IF 1.1 3区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Network-Computation in Neural Systems Pub Date : 2024-04-22 DOI:10.1080/0954898x.2024.2343348

Subhashini Ramachandran, Rajasekar Velusamy, Namakkal Venkataraman Srinivasan Sree Rathna Lakshmi, Chakaravarthi Sivanandam

引用次数: 0

Abstract

Cancer-related deadly diseases affect both developed and underdeveloped nations worldwide. Effective network learning is crucial to more reliably identify and categorize breast carcinoma in vast an...

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利用具有关注机制的多尺度扩张登森网的创新型乳腺癌检测框架

与癌症相关的致命疾病影响着全世界的发达国家和欠发达国家。有效的网络学习对于更可靠地识别和分类广大妇女中的乳腺癌至关重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Network-Computation in Neural Systems 工程技术-工程：电子与电气

CiteScore

3.70

自引率

1.30%

发文量

审稿时长

>12 weeks

期刊介绍： Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas: Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function. Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications. Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis. Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals. Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET. Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.