利用深度递归神经网络进行鸟类松鼠优化以检测前列腺癌

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-07-01 DOI:10.1615/intjmultcompeng.2024050495

Goddumarri Vijay Kumar, Mohammed Ismail B, Bhaskara Reddy T, Mansour Tahernezhadi, Mansoor Alam

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引用次数: 0

摘要

前列腺癌是一种实体器官黑色素瘤，会增加人类的死亡率。通过磁共振图像（MRI）确定前列腺癌的自动技术备受推崇。传统技术采用不同的步骤，这可能会导致巨大的计算成本。为了利用核磁共振成像进行前列腺癌自动分类，本研究开发了一种深度模型。在此，使用非局部均值（NLM）滤波器去除 MRI 噪声。卷积神经网络（CNN）也被广泛用于创建片段以提取显著特征，并被用于检测前列腺癌的深度递归神经网络（Deep RNN）。为了训练分类器，使用了所提出的鸟松鼠（BS）算法。通过结合鸟搜索算法（BSA）和松鼠搜索算法（SSA），创建了 BS。所提出的 BS-DeepRNN 具有更高的准确度（0.937）、灵敏度（0.958）和特异度（0.916），提高了效率。

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Bird Squirrel Optimization with Deep Recurrent Neural Network forProstate Cancer Detection

Prostate cancer is solid organ melanoma which increases mortality amongst humans. Automatic techniques for determining prostate cancer from magnetic resonance images (MRI) are highly recommended. Conventional techniques adapt different steps, which may result in huge computational costs. In order to perform automated prostate cancer classification with MRI, a deep model is developed in this research. Here, the MRI noise is removed using a Non-local Means (NLM) filter. Convolution neural networks (CNN) are also widely used to create segments in order to extract notable features, and they are used in deep recurrent neural networks (Deep RNN) for detecting prostate cancer. To train the classifier, the proposed Bird Squirrel (BS) algorithm is used. By combining the Bird search algorithm (BSA) and Squirrel search algorithm(SSA), the created BS is produced. With a higher accuracy of 0.937, a sensitivity of 0.958, and a specificity of 0.916, the proposed BS-DeepRNN enhanced efficiency.

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.