Abstract
With the rapid development of Artificial Intelligence (AI), deep learning has increasingly become a research hotspot in various fields, such as medical image classification. Traditional deep learning models use Bilinear Interpolation when processing classification tasks of multi-size medical image dataset, which will cause the loss of information of the image, and then affect the classification effect. In response to this problem, this work proposes a solution for an adaptive size deep learning model. First, according to the characteristics of the multi-size medical image dataset, the optimal size set module is proposed in combination with the unpooling process. Next, an adaptive deep learning model module is proposed based on the existing deep learning model. Then, the model is fused with the size fine-tuning module used to process multi-size medical images to obtain a solution of the adaptive size deep learning model. Finally, the proposed solution model is applied to the pneumonia CT medical image dataset. Through experiments, it can be seen that the model has strong robustness, and the classification effect is improved by about 4% compared with traditional algorithms.
- [1] Siam University, Bangkok, Bangkok, TH. 2017. Artificial intelligence, machine learning and deep learning. 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE), 1–6.
DOI: DOI: https://doi.org/10.1109/ICTKE.2017.8259629Google ScholarCross Ref
- [2] . 2019. Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies. Transportation Research Part C-emerging Technologies 105:297–322.
DOI: DOI: https://doi.org/10.1016/j.trc.2019.05.039Google ScholarCross Ref
- [3] . 2019. Application of deep learning to cybersecurity: A survey. Neurocomputing 347 (2019), 149–176.
DOI: DOI: https://doi.org/10.1016/j.neucom.2019.02.056Google ScholarDigital Library
- [4] . 2019. Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods 166, 4–21.
DOI: DOI: https://doi.org/10.1101/563601Google ScholarCross Ref
- [5] . 2016. Classification of medical image modeling methods: A review. Current Medical Imaging Reviews 12, 2 (2016), 130–148.
DOI: DOI: https://doi.org/10.2174/1573405612666160128235350Google ScholarCross Ref
- [6] . 2020. Advances in multimodal data fusion in Neuroimaging: Overview, challenges, and novel orientation, information fusion 64 (2020), 149–187.
DOI:
https://doi.org/10.1016/j.inffus.2020.07.006Google Scholar
- [7] . 2017. ChestX-Ray8: Hospital-Scale Chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3462–3471.
DOI: DOI: https://doi.org/10.1109/cvpr.2017.369Google ScholarCross Ref
- [8] 2015. ImageNet large scale visual recognition challenge. International Journal of Computer Vision 115, 3 (2015), 211–252.
DOI: DOI: https://doi.org/10.1007/s11263-015-0816-y Google ScholarDigital Library
- [9] . 2017. A survey on deep learning in medical image analysis. Medical Image Analysis 42 (2017), 60–88.
DOI: DOI: https://doi.org/10.1016/j.media.2017.07.005Google ScholarCross Ref
- [10] . 2016. Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library. O'Reilly Media, Inc. Google Scholar
Digital Library
- [11] . 2018. Infrared and visible image fusion using a deep learning framework. 2018 24th International Conference on Pattern Recognition (ICPR), 2705–2710.
DOI: DOI: https://doi.org/10.1109/icpr.2018.8546006Google ScholarCross Ref
- [12] . 2017. ImageNet classification with deep convolutional neural networks. Communications of The ACM 60, 6 (2017), 84–90.
DOI: DOI: https://doi.org/10.1145/3065386 Google ScholarDigital Library
- [13] . 2015. Very deep convolutional networks for large-scale image recognition. Computer Vision and Pattern Recognition. Retrieved from https://arxiv.org/abs/1409.1556.Google Scholar
- [14] . 2015. Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1–9.
DOI: DOI: https://doi.org/10.1109/cvpr.2015.7298594Google ScholarCross Ref
- [15] . 2016. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778.
DOI: DOI: https://doi.org/10.1109/cvpr.2016.90Google ScholarCross Ref
- [16] . 2019. EfficientNet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning, 6105–6114. Retrieved from
DOI:
https://arxiv.org/abs/1905.11946v4.Google Scholar
- [17] . 2019. Pathological brain detection based on AlexNet and transfer learning. Journal of Computational Science 30:41–47.
DOI:
https://doi.org/10.1016/j.jocs.2018.11.008Google Scholar
Cross Ref
- [18] . 2021. Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Information Fusion 67 (2021), 208–229.
DOI:
https://doi.org/10.1016/j.inffus.2020.10.004Google Scholar
Cross Ref
- [19] . 2021. COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis. Information Fusion 68 (2021), 131–148.
DOI:
https://doi.org/10.1016/j.inffus.2020.11.005Google Scholar
Cross Ref
- [20] . 2019. Alcoholism identification based on an AlexNet transfer learning model. Frontiers in Psychiatry 10 (2019), 205.
DOI: DOI: https://doi.org/10.3389/fpsyt.2019.00205Google ScholarCross Ref
- [21] . 2019. Classification of skin lesions using transfer learning and augmentation with Alex-net. Foaud 14, 5.
DOI: DOI: https://doi.org/10.1371/journal.pone.0217293Google Scholar - [22] . 2020. DC-AL GAN: Pseudo progression and true tumor progression of glioblastoma multiform image classification based on DCGAN and AlexNet. Medical Physics 47, 3 (2020), 1139–1150.
DOI: DOI: https://doi.org/10.1002/mp.14003Google ScholarCross Ref
- [23] . 2019. Lung segmentation method with dilated convolution based on VGG-16 network. Computer-assisted surgery (Abingdon, England) 24, 27–33.
DOI: DOI: https://doi.org/10.1080/24699322.2019.1649071Google ScholarCross Ref
- [24] . 2019. Automated brain image classification based on VGG-16 and transfer learning. 2019 International Conference on Information Technology (ICIT), 94–98.
DOI: DOI: https://doi.org/10.1109/icit48102.2019.00023Google ScholarCross Ref
- [25] . 2017. Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database. PLOS ONE 12, 11 (2017).
DOI: DOI: https://doi.org/10.1371/journal.pone.0187336Google ScholarCross Ref
- [26] . 2020. Hybrid fully convolutional networks-based skin lesion segmentation and melanoma detection using the deep feature. International Journal of Imaging Systems and Technology 30, 2 (2020), 348–357.
DOI: DOI: https://doi.org/10.1002/ima.22377Google ScholarCross Ref
- [27] . 2019. NHL pathological image classification based on hierarchical local information and GoogLeNet-Based representations. BioMed Research International 2019: 1065652.
DOI: DOI: https://doi.org/10.1155/2019/1065652Google ScholarCross Ref
- [28] . 2017. Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. Journal of Digital Imaging 30, 4 (2017), 477–486.
DOI: DOI: https://doi.org/10.1007/s10278-017-9997-yGoogle ScholarCross Ref
- [29] . 2018. Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review. Journal of The American Academy of Dermatology 81, 5 (2018), 1176–1180.
DOI: DOI: https://doi.org/10.1016/j.jaad.2019.06.042Google ScholarCross Ref
- [30] . 2020. Automatic diagnosis of melanoma using hyperspectral data and GoogLeNet. Skin Research and Technology 26, 6 (2020), 891–897.
DOI: DOI: https://doi.org/10.1111/srt.12891Google ScholarCross Ref
- [31] . 2020. A GPU-based residual network for medical image classification in smart medicine. Information Sciences 536 (2020), 91–100.
DOI: DOI: https://doi.org/10.1016/j.ins.2020.05.013Google ScholarCross Ref
- [32] . 2020. The classification of gliomas based on a Pyramid dilated convolution resnet model. Pattern Recognition Letters 133 (2020), 173–179.
DOI: DOI: https://doi.org/10.1016/j.patrec.2020.03.007Google ScholarCross Ref
- [33] . 2020. Combining DC-GAN with ResNet for blood cell image classification. Medical & Biological Engineering & Computing 58, 6 (2020), 1251–1264.
DOI: DOI: https://doi.org/10.1007/s11517-020-02163-3Google ScholarCross Ref
- [34] . 2018. Large dataset of labeled optical coherence tomography (OCT) and Chest X-Ray Images, Mendeley Data, V3.
DOI: DOI: https://doi.org/10.17632/rscbjbr9sj.3Google Scholar - [35] . 2021. Contour feature extraction of medical image based on multi-threshold optimization. Mobile Networks and Applications 26, 1 (2021 Feb), 381–389.
DOI:
https://doi.org/10.1007/s11036-020-01674-5Google Scholar
Cross Ref
- [36] . 2021. Agent architecture of an intelligent medical system based on federated learning and blockchain technology. Journal of Information Security and Applications 1, 58 (2021 May), 102748.
DOI:
https://doi.org/10.1016/j.jisa.2021.102748Google Scholar
Cross Ref
Index Terms
Medical Image Classification based on an Adaptive Size Deep Learning Model
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Graphical abstractDisplay Omitted
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