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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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9617630/ |
| _version_ | 1830272884463894528 |
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| author | Bekhzod Olimov Seok-Joo Koh Jeonghong Kim |
| author_facet | Bekhzod Olimov Seok-Joo Koh Jeonghong Kim |
| author_sort | Bekhzod Olimov |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-18T23:35:01Z |
| format | Article |
| id | doaj.art-5a00f2f25a5f4e15940e37a144bf41c4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T23:35:01Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5a00f2f25a5f4e15940e37a144bf41c42022-12-21T20:47:35ZengIEEEIEEE Access2169-35362021-01-01915419415420310.1109/ACCESS.2021.31286079617630AEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image SegmentationBekhzod Olimov0https://orcid.org/0000-0003-1090-0910Seok-Joo Koh1https://orcid.org/0000-0003-3429-2040Jeonghong Kim2https://orcid.org/0000-0002-7466-1376Computer Science and Engineering Department, Kyungpook National University, Daegu, South KoreaComputer Science and Engineering Department, Kyungpook National University, Daegu, South KoreaComputer Science and Engineering Department, Kyungpook National University, Daegu, South KoreaImage 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.https://ieeexplore.ieee.org/document/9617630/Computational efficiencydeep convolutional neural networksmedical image segmentation |
| spellingShingle | Bekhzod Olimov Seok-Joo Koh Jeonghong Kim AEDCN-Net: Accurate and Efficient Deep Convolutional Neural Network Model for Medical Image Segmentation IEEE Access Computational efficiency deep convolutional neural networks medical image segmentation |
| title | 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_short | 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 |
| topic | Computational efficiency deep convolutional neural networks medical image segmentation |
| url | https://ieeexplore.ieee.org/document/9617630/ |
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