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Alzheimer’s disease classification using distilled multi-residual network

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Abstract

Early human intervention is crucial for diagnosing Alzheimer’s Disease (AD), since AD is irreversible and leads to progressive impairment of memory. In recent years, Convolutional Neural Networks (CNNs) have achieved dramatic breakthroughs in AD diagnosis. However, existing CNNs have difficulties in extracting subtle contextual information because of their structural limitations, i.e., it is difficult to extract discriminative features of several regions, such as hippocampus, parietal, temporal lobe tissues, and so on. In addition, current networks have difficulty in classifying imaging features with imbalanced data categories. Some loss functions can alleviate the above problems to some extent, but they are affected by outliers and trapped in local optimum easily. To address this issue, a Distilled Multi-Residual Network (DMRNet) is proposed for the early diagnosis of AD. The DMRNet consists of three main components: 1) Dense Connection Block, 2) Focus Attention Block, and 3) Multi-scale Fusion Module. The dense features are extracted by the Dense Connection Block while the local abnormal regions are refined by the Focus Attention Block and Multi-scale Fusion Module. Besides, to explore the hidden knowledge between each feature, a dilated classifier with self-distillation is proposed to ensemble several items pertaining to feature knowledge from feature space. Finally, the Remix Balance Sampler (RBS) is proposed to alleviate the influences of outliers. The proposed DMRNet is evaluated on baseline sMRI scans of the ADNI dataset. The result of experiments demonstrated that the proposed DMRNet not only achieves 7.15% greater accuracy than state-of-the-art methods but also successfully identifies some AD-related regions.

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References

  1. Association A (2019) 2019 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia 15 (3):321–387

    Article  Google Scholar 

  2. Chaves R, Ramírez J, Górriz J, López M, Salas-Gonzalez D, Alvarez I, Segovia F (2009) Svm-based computer-aided diagnosis of the alzheimer’s disease using t-test nmse feature selection with feature correlation weighting. Neurosci Lett 461(3):293–297

    Article  Google Scholar 

  3. Yang ST, Lee JD, Chang TC, Huang CH, Wang JJ, Hsu WC, Chan HL, Wai YY, Li KY (2013) Discrimination between alzheimer’s disease and mild cognitive impairment using som and pso-svm. Comput Math Methods Med 2013:253670

    Article  MathSciNet  Google Scholar 

  4. Xu Y, Pan X, Zhou Z, Yang Z, Zhang Y (2015) Structural least square twin support vector machine for classification. Appl Intell 42(3):527–536

    Article  Google Scholar 

  5. Nanni L, Brahnam S, Salvatore C, Castiglioni I, Initiative ADN (2019) Texture descriptors and voxels for the early diagnosis of alzheimer’s disease. Artif Intell Med 97:19–26

    Article  Google Scholar 

  6. Gupta Y, Lee KH, Choi KY, Lee JJ, Kim BC, Kwon GR (2019) For dementia, n.r.c., initiative, a.d.n early diagnosis of alzheimer’s disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of mri t1 brain images. PLoS One 14(10):0222446

    Article  Google Scholar 

  7. Zhu T, Cao C, Wang Z, Xu G, Qiao J (2020) Anatomical landmarks and dag network learning for alzheimer’s disease diagnosis. IEEE Access 8:206063–206073

    Article  Google Scholar 

  8. Moosaei H, Bazikar F, Ketabchi S, Hladík M (2022) Universum parametric-margin ν-support vector machine for classification using the difference of convex functions algorithm. appl intell 52 (3):2634–2654

    Article  Google Scholar 

  9. Kaplan E, Dogan S, Tuncer T, Baygin M, Altunisik E (2021) Feed-forward lpqnet based automatic alzheimer’s disease detection model. Comput Biol Med 137:104828

    Article  Google Scholar 

  10. Dolz J, Desrosiers C, Ayed IB (2018) 3D fully convolutional networks for subcortical segmentation in mri: a large-scale study. Neuroimage 170:456–470

    Article  Google Scholar 

  11. Gunawardena K, Rajapakse R, Kodikara N (2017) Applying convolutional neural networks for pre-detection of alzheimer’s disease from structural mri data. In: 2017 24th International conference on mechatronics and machine vision in practice (M2VIP), pp 1–7. IEE

  12. Ji J, Yao Y (2022) A novel cnn framework to extract multi-level modular features for the classification of brain networks. Appl Intell 52(6):6835–6852

    Article  Google Scholar 

  13. Kong B, Wang X, Li Z, Song Q, Zhang S (2017) Cancer metastasis detection via spatially structured deep network. In: International conference on information processing in medical imaging, pp 236–248. Springer

  14. Wang SH, Zhou Q, Yang M, Zhang YD (2021) Advian: Alzheimer’s disease vgg-inspired attention network based on convolutional block attention module and multiple way data augmentation. Front Aging Neurosci 13:313–327

    Google Scholar 

  15. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  16. Fulton LV, Dolezel D, Harrop J, Yan Y, Fulton CP (2019) Classification of alzheimer’s disease with and without imagery using gradient boosted machines and resnet-50. Brain Sci 9(9):212–227

    Article  Google Scholar 

  17. Cui R, Liu M (2018) Hippocampus analysis by combination of 3-d densenet and shapes for alzheimer’s disease diagnosis. IEEE J Biomed Health Inform 23(5):2099–2107

    Article  Google Scholar 

  18. Lu X, Wu H, Zeng Y (2019) Classification of alzheimer’s disease in mobilenet. In: Journal of physics: conference series, vol 1345, pp 042012. IOP Publishing

  19. Murugan S, Venkatesan C, Sumithra M, Gao XZ, Elakkiya B, Akila M, Manoharan S (2021) Demnet: a deep learning model for early diagnosis of alzheimer diseases and dementia from mr images. IEEE Access 9:90319–90329

    Article  Google Scholar 

  20. Faruqui N, Yousuf MA, Whaiduzzaman M, Azad A, Barros A, Moni MA (2021) Lungnet: a hybrid deep-cnn model for lung cancer diagnosis using ct and wearable sensor-based medical iot data. Comput Biol Med 139:104961

    Article  Google Scholar 

  21. Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers R.M (2017) Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2097–2106

  22. Lin TY, Goyal P, Girshick R, He K, Dollár P. (2017) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell PP(99):2999–3007

    Google Scholar 

  23. Li B, Liu Y, Wang X (2019) Gradient harmonized single-stage detector. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 8577–8584

  24. Cui Y, Jia M, Lin TY, Song Y, Belongie S (2019) Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9268–9277

  25. Rasti R, Rabbani H, Mehridehnavi A, Hajizadeh F (2017) Macular oct classification using a multi-scale convolutional neural network ensemble. IEEE Trans Med Imaging 37(4):1024–1034

    Article  Google Scholar 

  26. Sheikh TS, Lee Y, Cho M (2020) Histopathological classification of breast cancer images using a multi-scale input and multi-feature network. Cancers 12(8):2031–2050

    Article  Google Scholar 

  27. Ullah H, Zhao Y, Abdalla FY, Wu L (2022) Fast local laplacian filtering based enhanced medical image fusion using parameter-adaptive pcnn and local features-based fuzzy weighted matrices. Appl Intell 52 (7):7965–7984

    Article  Google Scholar 

  28. Khvostikov A, Aderghal K, Benois-Pineau J, Krylov A, Catheline G (2018) 3d cnn-based classification using smri and md-dti images for alzheimer disease studies. arXiv:1801.05968

  29. Fan T, Wang G, Li Y, Wang H (2020) Ma-net: A multi-scale attention network for liver and tumor segmentation. IEEE Access 8:179656–179665

    Article  Google Scholar 

  30. Fu J, Li W, Du J, Huang Y (2021) A multiscale residual pyramid attention network for medical image fusion. Biomed Signal Process Control 66:102488

    Article  Google Scholar 

  31. Fong JX, Shapiai MI, Tiew YY, Batool U, Fauzi H (2020) Bypassing mri pre-processing in alzheimer’s disease diagnosis using deep learning detection network. In: 2020 16th IEEE International colloquium on signal processing & its applications (CSPA), pp 219–224. IEEE

  32. Mok TC, Chung A (2020) Large deformation diffeomorphic image registration with laplacian pyramid networks. In: International conference on medical image computing and computer-assisted intervention, pp 211–221. Springer

  33. Fu J, Li W, Du J, Xiao B (2020) Multimodal medical image fusion via laplacian pyramid and convolutional neural network reconstruction with local gradient energy strategy. Comput Biol Med 126:104048

    Article  Google Scholar 

  34. Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125

  35. Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) Smoteboost: Improving prediction of the minority class in boosting. In: European conference on principles of data mining and knowledge discovery, pp 107–119. Springer

  36. Jesson A, Guizard N, Ghalehjegh SH, Goblot D, Soudan F, Chapados N (2017) Cased: curriculum adaptive sampling for extreme data imbalance. In: International conference on medical image computing and computer-assisted intervention, pp 639–646. Springer

  37. Torres FR, Carrasco-Ochoa JA, Martínez-Trinidad J.F (2016) Smote-d a deterministic version of smote. In: Mexican conference on pattern recognition, pp 177–188. Springer

  38. Gao L, Zhang L, Liu C, Wu S (2020) Handling imbalanced medical image data: a deep-learning-based one-class classification approach. Artif Intell Med 108:101935

    Article  Google Scholar 

  39. Zhang H, Zhang H, Pirbhulal S, Wu W, Albuquerque VHCD (2020) Active balancing mechanism for imbalanced medical data in deep learning–based classification models. ACM Trans Multimed Comput Commun Appl (TOMM) 16(1s):1–15

    Article  Google Scholar 

  40. Liu Q, Liu H, Zhao Y, Liang Y (2021) Dual-branch network with dual-sampling modulated dice loss for hard exudate segmentation in color fundus images. IEEE J Biomed Health Inform 26(3):1091–1102

    Article  Google Scholar 

  41. Jin D, Xu J, Zhao K, Hu F, Yang Z, Liu B, Jiang T, Liu Y (2019) Attention-based 3d convolutional network for alzheimer’s disease diagnosis and biomarkers exploration. In: 2019 IEEE 16th International symposium on biomedical imaging (ISBI 2019), pp 1047–1051. IEEE

  42. Feng C, Elazab A, Yang P, Wang T, Zhou F, Hu H, Xiao X, Lei B (2019) Deep learning framework for alzheimer’s disease diagnosis via 3d-cnn and fsbi-lstm. IEEE Access 7:63605–63618

    Article  Google Scholar 

  43. Yee E, Ma D, Popuri K, Wang L, Beg MF, Initiative ADN (2021) Construction of mri-based alzheimer’s disease score based on efficient 3d convolutional neural network: comprehensive validation on 7,902 images from a multi-center dataset. J Alzheimers Dis 79(1):47–58

    Article  Google Scholar 

  44. Esmaeilzadeh S, Belivanis DI, Pohl KM, Adeli E (2018) End-to-end alzheimer’s disease diagnosis and biomarker identification. In: International workshop on machine learning in medical imaging, pp 337–345. Springer

  45. Korolev S, Safiullin A, Belyaev M, Dodonova Y (2017) Residual and plain convolutional neural networks for 3d brain mri classification. In: 2017 IEEE 14th International symposium on biomedical imaging (ISBI 2017), pp 835–838. IEEE

  46. Liu M, Zhang J, Adeli E, Shen D (2018) Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal 43:157–168

    Article  Google Scholar 

  47. Cui R, Liu M, Initiative ADN (2019) Rnn-based longitudinal analysis for diagnosis of alzheimer’s disease. Comput Med Imaging Graph 73:1–10

    Article  Google Scholar 

  48. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618–626

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Funding

This work was sponsored in part by the Key-Area Research and Development Program of Guangdong Province under Grant 2019B010109001, in part by Guangdong Natural Science Foundation under Grant 2020A1515011409, in part by Construction Project of Regional Innovation Capability and Support Guarantee System in Guangdong Province under Grant 2021A1414030004, in part by Provincial Agricultural Science and technology innovation and Extension project of Guangdong Province under Grant 2022KJ147.

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Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China

    Xuehu Liang, Zhuowei Wang & Ziyang Chen

  2. Department of Electrical and Computer Engineering, Portland State University, Portland, 97207, Oregon, USA

    Xiaoyu Song

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  1. Xuehu Liang
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Correspondence to Zhuowei Wang.

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Liang, X., Wang, Z., Chen, Z. et al. Alzheimer’s disease classification using distilled multi-residual network. Appl Intell 53, 11934–11950 (2023). https://doi.org/10.1007/s10489-022-04084-0

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