AEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image Segmentation

Image segmentation was significantly enhanced after the emergence of deep learning (DL) methods. In particular, deep convolutional neural networks (DCNNs) have assisted DL-based segmentation models to achieve state-of-the-art performance in fields critical to human beings, such as medicine. However,...

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Autores principales:	Bekhzod Olimov, Seok-Joo Koh, Jeonghong Kim
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Computational efficiency deep convolutional neural networks medical image segmentation Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/5a00f2f25a5f4e15940e37a144bf41c4
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spelling	oai:doaj.org-article:5a00f2f25a5f4e15940e37a144bf41c42021-11-24T00:03:23ZAEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image Segmentation2169-353610.1109/ACCESS.2021.3128607https://doaj.org/article/5a00f2f25a5f4e15940e37a144bf41c42021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9617630/https://doaj.org/toc/2169-3536Image segmentation was significantly enhanced after the emergence of deep learning (DL) methods. In particular, deep convolutional neural networks (DCNNs) have assisted DL-based segmentation models to achieve state-of-the-art performance in fields critical to human beings, such as medicine. However, the existing state-of-the-art methods often use computationally expensive operations to achieve high accuracy and lightweight networks often lack a precise medical image segmentation. Therefore, this study proposes an accurate and efficient DCNN model (AEDCN-Net) based on an elaborate preprocessing step and a resourceful model architecture. The AEDCN-Net exploits bottleneck, atrous, and asymmetric convolution-based residual skip connections in the encoding path that reduce the number of trainable parameters and floating point operations (FLOPs) to learn feature representations with a larger receptive field. The decoding path employs the nearest-neighbor based upsampling method instead of a computationally resourceful transpose convolution operation that requires an extensive number of trainable parameters. The proposed method attains a superior performance in both computational time and accuracy compared to the existing state-of-the-art methods. The results of benchmarking using four real-life medical image datasets specifically illustrate that the AEDCN-Net has a faster convergence compared to the computationally expensive state-of-the-art models while using significantly fewer trainable parameters and FLOPs that result in a considerable speed-up during inference. Moreover, the proposed method obtains a better accuracy in several evaluation metrics compared with the existing lightweight and efficient methods.Bekhzod OlimovSeok-Joo KohJeonghong KimIEEEarticleComputational efficiencydeep convolutional neural networksmedical image segmentationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154194-154203 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computational efficiency deep convolutional neural networks medical image segmentation Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Computational efficiency deep convolutional neural networks medical image segmentation Electrical engineering. Electronics. Nuclear engineering TK1-9971 Bekhzod Olimov Seok-Joo Koh Jeonghong Kim AEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image Segmentation
description	Image segmentation was significantly enhanced after the emergence of deep learning (DL) methods. In particular, deep convolutional neural networks (DCNNs) have assisted DL-based segmentation models to achieve state-of-the-art performance in fields critical to human beings, such as medicine. However, the existing state-of-the-art methods often use computationally expensive operations to achieve high accuracy and lightweight networks often lack a precise medical image segmentation. Therefore, this study proposes an accurate and efficient DCNN model (AEDCN-Net) based on an elaborate preprocessing step and a resourceful model architecture. The AEDCN-Net exploits bottleneck, atrous, and asymmetric convolution-based residual skip connections in the encoding path that reduce the number of trainable parameters and floating point operations (FLOPs) to learn feature representations with a larger receptive field. The decoding path employs the nearest-neighbor based upsampling method instead of a computationally resourceful transpose convolution operation that requires an extensive number of trainable parameters. The proposed method attains a superior performance in both computational time and accuracy compared to the existing state-of-the-art methods. The results of benchmarking using four real-life medical image datasets specifically illustrate that the AEDCN-Net has a faster convergence compared to the computationally expensive state-of-the-art models while using significantly fewer trainable parameters and FLOPs that result in a considerable speed-up during inference. Moreover, the proposed method obtains a better accuracy in several evaluation metrics compared with the existing lightweight and efficient methods.
format	article
author	Bekhzod Olimov Seok-Joo Koh Jeonghong Kim
author_facet	Bekhzod Olimov Seok-Joo Koh Jeonghong Kim
author_sort	Bekhzod Olimov
title	AEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image Segmentation
title_short	AEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image Segmentation
title_full	AEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image Segmentation
title_fullStr	AEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image Segmentation
title_full_unstemmed	AEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image Segmentation
title_sort	aedcn-net: accurate and efficient deep convolutional neural network model for medical image segmentation
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/5a00f2f25a5f4e15940e37a144bf41c4
work_keys_str_mv	AT bekhzodolimov aedcnnetaccurateandefficientdeepconvolutionalneuralnetworkmodelformedicalimagesegmentation AT seokjookoh aedcnnetaccurateandefficientdeepconvolutionalneuralnetworkmodelformedicalimagesegmentation AT jeonghongkim aedcnnetaccurateandefficientdeepconvolutionalneuralnetworkmodelformedicalimagesegmentation
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AEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image Segmentation

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