Multi-Channel CNN based image classification using SKIP connection and MSVM

被引：0

作者：

Kesav N. ^{[1
]}

Jibukumar M.G. ^{[1
]}

机构：

[1] Division of Electronics and Communication, School of Engineering, Cochin University of Science & Technology, Kochi

来源：

International Journal of Computers and Applications | 2022年 / 44卷 / 10期

关键词：

complexity reduction; Deep learning; multi-channel CNN; SVM;

D O I：

10.1080/1206212X.2022.2047443

中图分类号：

学科分类号：

摘要：

Deep learning innovations have paved way for effective classification algorithms using the Convolutional Neural Networks (CNNs). The current scenario uses very deep networks to improve the overall efficiency. This deep nature will result in increased complexity, a high number of parameters, increased execution time, and a more complex hardware platform for execution. Our research focuses on minimizing this complex nature of architecture. To achieve this, we employed the multi-channel CNN with a shallow layers approach, which consists of the main channel and side channels. The proposed work uses the Multi class Support Vector Machione (MSVM) as classifier and three distinct architectures with varied filter widths to acquire different performance characteristics. All these models are trained and tested on a brain tumor type database and performance parameters are compared to deep architectures like the Alexnet, VGG16, VGG19, and Resnet 50. When compared to deep architectures for the same database, our model can reduce the overall number of parameters and execution time with comparable accuracy. To improve the overall efficiency, our final architecture includes a skip connection. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

引用

页码：981 / 990

页数：9

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