Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet
Medical image classification plays an important role in disease diagnosis since it can provide important reference information for doctors. The supervised convolutional neural networks (CNNs) such as DenseNet provide the versatile and effective method for medical image classification tasks, but they...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8979430/ |
| _version_ | 1818857087018467328 |
|---|---|
| author | Zhiwen Huang Xingxing Zhu Mingyue Ding Xuming Zhang |
| author_facet | Zhiwen Huang Xingxing Zhu Mingyue Ding Xuming Zhang |
| author_sort | Zhiwen Huang |
| collection | DOAJ |
| description | Medical image classification plays an important role in disease diagnosis since it can provide important reference information for doctors. The supervised convolutional neural networks (CNNs) such as DenseNet provide the versatile and effective method for medical image classification tasks, but they require large amounts of data with labels and involve complex and time-consuming training process. The unsupervised CNNs such as principal component analysis network (PCANet) need no labels for training but cannot provide desirable classification accuracy. To realize the accurate medical image classification in the case of a small training dataset, we have proposed a light-weighted hybrid neural network which consists of a modified PCANet cascaded with a simplified DenseNet. The modified PCANet has two stages, in which the network produces the effective feature maps at each stage by convoluting inputs with various learned kernels. The following simplified DenseNet with a small number of weights will take all feature maps produced by the PCANet as inputs and employ the dense shortcut connections to realize accurate medical image classification. To appreciate the performance of the proposed method, some experiments have been done on mammography and osteosarcoma histology images. Experimental results show that the proposed hybrid neural network is easy to train and it outperforms such popular CNN models as PCANet, ResNet and DenseNet in terms of classification accuracy, sensitivity and specificity. |
| first_indexed | 2024-12-19T08:34:49Z |
| format | Article |
| id | doaj.art-bfec77e5f89444b69ca1df81d1bf71c5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:34:49Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bfec77e5f89444b69ca1df81d1bf71c52022-12-21T20:29:04ZengIEEEIEEE Access2169-35362020-01-018246972471210.1109/ACCESS.2020.29712258979430Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNetZhiwen Huang0https://orcid.org/0000-0003-3021-5662Xingxing Zhu1https://orcid.org/0000-0002-2541-8222Mingyue Ding2https://orcid.org/0000-0003-3933-1205Xuming Zhang3https://orcid.org/0000-0003-4332-071XDepartment of Biomedical Engineering, Key Laboratory of Molecular Biophysics, School of Life Science and Technology, Ministry of Education, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Biomedical Engineering, Key Laboratory of Molecular Biophysics, School of Life Science and Technology, Ministry of Education, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Biomedical Engineering, Key Laboratory of Molecular Biophysics, School of Life Science and Technology, Ministry of Education, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Biomedical Engineering, Key Laboratory of Molecular Biophysics, School of Life Science and Technology, Ministry of Education, Huazhong University of Science and Technology, Wuhan, ChinaMedical image classification plays an important role in disease diagnosis since it can provide important reference information for doctors. The supervised convolutional neural networks (CNNs) such as DenseNet provide the versatile and effective method for medical image classification tasks, but they require large amounts of data with labels and involve complex and time-consuming training process. The unsupervised CNNs such as principal component analysis network (PCANet) need no labels for training but cannot provide desirable classification accuracy. To realize the accurate medical image classification in the case of a small training dataset, we have proposed a light-weighted hybrid neural network which consists of a modified PCANet cascaded with a simplified DenseNet. The modified PCANet has two stages, in which the network produces the effective feature maps at each stage by convoluting inputs with various learned kernels. The following simplified DenseNet with a small number of weights will take all feature maps produced by the PCANet as inputs and employ the dense shortcut connections to realize accurate medical image classification. To appreciate the performance of the proposed method, some experiments have been done on mammography and osteosarcoma histology images. Experimental results show that the proposed hybrid neural network is easy to train and it outperforms such popular CNN models as PCANet, ResNet and DenseNet in terms of classification accuracy, sensitivity and specificity.https://ieeexplore.ieee.org/document/8979430/Medical image classificationhybrid neural networkPCANetDenseNet |
| spellingShingle | Zhiwen Huang Xingxing Zhu Mingyue Ding Xuming Zhang Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet IEEE Access Medical image classification hybrid neural network PCANet DenseNet |
| title | Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet |
| title_full | Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet |
| title_fullStr | Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet |
| title_full_unstemmed | Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet |
| title_short | Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet |
| title_sort | medical image classification using a light weighted hybrid neural network based on pcanet and densenet |
| topic | Medical image classification hybrid neural network PCANet DenseNet |
| url | https://ieeexplore.ieee.org/document/8979430/ |
| work_keys_str_mv | AT zhiwenhuang medicalimageclassificationusingalightweightedhybridneuralnetworkbasedonpcanetanddensenet AT xingxingzhu medicalimageclassificationusingalightweightedhybridneuralnetworkbasedonpcanetanddensenet AT mingyueding medicalimageclassificationusingalightweightedhybridneuralnetworkbasedonpcanetanddensenet AT xumingzhang medicalimageclassificationusingalightweightedhybridneuralnetworkbasedonpcanetanddensenet |