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

Abstract

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.

KEYWORDS:

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

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Nivea Kesav

Ms. Nivea Kesav: A B. Tech graduate from Rajagiri School of Engineering and Technology, Kerala and an M. Tech graduate with Distinction from Cochin University of Science and Technology, Kerala, India. Currently pursuing Ph.D. under the guidance of Dr. Jibukumar M.G at CUSAT, India. Area of expertise includes Machine learning, Image processing, Biomedical image analysis, Wireless Communication.

M.G. Jibukumar

Dr. Jibukumar M. G: A B. Tech graduate from M.A College, Kothamangalam, Kerala and an M. Tech graduate from Cochin University of Science and Technology, Kerala, India. Completed Ph.D. under the guidance of Dr. P.K Biswas from IIT Kharagpur and currently working as a Professor at CUSAT and also as a Ph.D. mentor for research scholars. Area of expertise includes Wireless communication, Protocol development, WLAN, Machine learning, Biomedical Image processing, Wireless energy harvesting, Physical layer security, Photonics etc.