DKCNN: Improving deep kernel convolutional neural network-based COVID-19 identification from CT images of the chest.

IF 1.7 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION
T Vaikunta Pai, K Maithili, Ravula Arun Kumar, D Nagaraju, D Anuradha, Shailendra Kumar, Ananda Ravuri, T Sunilkumar Reddy, M Sivaram, R G Vidhya
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引用次数: 0

Abstract

Background: An efficient deep convolutional neural network (DeepCNN) is proposed in this article for the classification of Covid-19 disease.

Objective: A novel structure known as the Pointwise-Temporal-pointwise convolution unit is developed incorporated with the varying kernel-based depth wise temporal convolution before and after the pointwise convolution operations.

Methods: The outcome is optimized by the Slap Swarm algorithm (SSA). The proposed Deep CNN is composed of depth wise temporal convolution and end-to-end automatic detection of disease. First, the datasets SARS-COV-2 Ct-Scan Dataset and CT scan COVID Prediction dataset are preprocessed using the min-max approach and the features are extracted for further processing.

Results: The experimental analysis is conducted between the proposed and some state-of-art works and stated that the proposed work effectively classifies the disease than the other approaches.

Conclusion: The proposed structural unit is used to design the deep CNN with the increasing kernel sizes. The classification process is improved by the inclusion of depth wise temporal convolutions along with the kernel variation. The computational complexity is reduced by the introduction of stride convolutions are used in the residual linkage among the adjacent structural units.

DKCNN:从胸部 CT 图像中改进基于深度核卷积神经网络的 covid-19 识别。
背景:本文提出了一种高效的深度卷积神经网络(DeepCNN),用于 Covid-19 疾病的分类:开发了一种称为点时卷积单元(Pointwise-Temporal-pointwise convolution unit)的新型结构,并在点时卷积操作前后加入了基于内核的不同深度时序卷积:方法:采用拍击蜂群算法(SSA)对结果进行优化。所提出的深度 CNN 由深度时空卷积和端到端疾病自动检测组成。首先,使用最小最大法对数据集 SARS-COV-2 Ct-Scan Dataset 和 CT 扫描 COVID 预测数据集进行预处理,并提取特征进行进一步处理:结果:对所提出的方法和一些先进的方法进行了实验分析,结果表明,与其他方法相比,所提出的方法能有效地对疾病进行分类:结论:提出的结构单元用于设计内核尺寸不断增大的深度 CNN。通过将深度时间卷积与内核变化结合起来,改进了分类过程。通过在相邻结构单元之间的残差联系中引入跨距卷积,降低了计算复杂度。
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来源期刊
CiteScore
4.90
自引率
23.30%
发文量
150
审稿时长
3 months
期刊介绍: Research areas within the scope of the journal include: Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes
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