Abstract
Alzheimer's disease is a progressive disease that weakens mind’s memory and overall functioning. In the early identification of Alzheimer's disease, neuroimaging is increasingly being utilized to support clinical examinations (AD). One of the most commonly utilized and promising modalities for detecting brain abnormalities in persons who may be at risk for AD but have not yet exhibited symptoms is structural magnetic resonance imaging (MRI). In this study, a transfer learning model called EfficietNetB7 architecture is analyzed to enhance the prediction with pre-trained weights in a benchmark dataset of neuroimages. The network is further fine-tuned via layer-wise tuning, which involves training a pre-defined set of layers using MRI images. The performance of the proposed system is evaluated over the Kaggle brain MRI dataset that includes four classes such as mild demented, moderately demented, non-demented, and very mildly demented. For AD classification, the proposed trained model achieves enhanced accuracy and F1-score as 89.7% and 0.91, respectively.
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Hwang, E.J., Park, S., Jin, K.N., Im Kim, J., Choi, S.Y., Lee, J.H., Goo, J.M., Aum, J., Yim, J.J., Cohen, J.G., Ferretti, G.R.: Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA 2(3), e191095 (2019)
Jawahar, M., Anbarasi, L.J., Jasmine, S.G., Narendra, M: Diabetic foot ulcer segmentation using color space models. In 2020 5th International Conference on Communication and Electronics Systems (ICCES), pp. 742–747. IEEE, (2020)
Sharon, J.J., Anbarasi, L.L.: Diagnosis of DCM and HCM heart diseases using neural network function. Int. J. Appl. Eng. Res. 13(10), 8664–8668 (2018)
Prajoth SenthilKumar, A.L., Narendra, M., Jani Anbarasi, L., Raj, B.E.: Breast cancer analysis and detection in histopathological images using CNN approach. In Proceedings of International Conference on Intelligent Computing, Information and Control Systems, pp. 335–343. Springer, Singapore, (2021)
Li, F., Tran, L., Thung, K.-H., Ji, S., Shen, D., Li, J.: A robust deep model for improved classification of AD/MCI patients. IEEE J. Biomed. Heal informatics. 19(5), 2168–2194 (2015)
https://analyticsindiamag.com/implementing-efficientnet-a-powerful-convolutional-neural-network
Pan, D., Zeng, A., Jia, L., Huang, Y., Frizzell, T., Song, X.: Early detection of Alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Front. Neurosci. 14 (May 2020)
Fanar E.K., Al-Khuzaie, Bayat, O., Duru, A.D.: Diagnosis of Alzheimer disease using 2d MRI slices by convolutional neural network. Appl. Bionics Biomech. 1(9), 6690539 (2021)
Jain, R., Jain, N., Aggarwal, A., Hemanth, D.J.: Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cogn. Syst. Res. 57, 147–159 (2019)
Nawaz, H., Maqsood, M., Afzal, S., Aadil, F., Mehmood, I., Rho, S.: A deep feature-based real-time system for Alzheimer disease stage detection. Multimedia Tools Appl. 1, 19 (2020)
Lin, W., Tong, T., Gao, Q., Guo, D., Du, X., Yang, Y., Guo, G., Xiao, M., Du, M., Qu, X. and Alzheimer’s Disease Neuroimaging Initiative: Convolutional neural networks-based MRI image analysis for the Alzheimer’s disease prediction from mild cognitive impairment. Front. Neurosci. 12 (Nov 2018)
Liu, M., Li, F., Yan, H., Wang, K., Ma, Y., Alzheimer’s Disease Neuroimaging Initiative, Shen, L., Xu, M.: A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. NeuroImage 208 (2020)
Islam, J., Zhang, Y.: Early diagnosis of alzheimer’s disease: a neuroimaging study with deep learning architectures. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE (June 2018)
Abrol, A., Fu, Z., Du, Y., Calhoun, V.D.: Multimodal data fusion of deep learning and dynamic functional connectivity features to predict Alzheimer’s disease progression. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE (July 2019)
Taheri Gorji, H., Kaabouch, N.: A deep learning approach for diagnosis of mild cognitive impairment based on MRI images. Brain Sci. 9(9), 217 (2019)
Aderghal, K., Khvostikov, A., Krylov, A., Benois-Pineau, J., Afdel, K., Catheline, G.: Classification of Alzheimer disease on imaging modalities with deep CNNs using cross-modal transfer learning. In: 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), IEEE (June 2018)
Nanni, L., Interlenghi, M., Brahnam, S., Salvatore, C., Papa, S., Nemni, R., Castiglioni, I., Alzheimer’s Disease Neuroimaging Initiative: Comparison of transfer learning and conventional machine learning applied to structural brain MRI for the early diagnosis and prognosis of Alzheimer’s disease. Front. Neurol. 11 (Nov 2020)
Ashraf, A., Naz, S., Shirazi, S.H., Razzak, I., Parsad, M.: Deep transfer learning for Alzheimer neurological disorder detection. Multimedia Tools Appl. 1(26) (2021)
Yagis, E., Citi, L., Diciotti, S., Marzi, C., Atnafu, S.W., De Herrera, A.G.S.: 3d convolutional neural networks for diagnosis of Alzheimer’s disease via structural MRI. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), IEEE (July 2020)
Raju, M., Gopi, V.P., Anitha, V.S., Wahid, K.A.: Wahid: multi-class diagnosis of Alzheimer’s disease using cascaded three dimensional-convolutional neural network. Phys. Eng. Sci. Med. 1(10), 1219–1228 (2020)
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning, pp. 6105–6114. PMLR, (2019)
https://ai.googleblog.com/2019/05/efficientnet-improving-accuracy-and.html
Stoeckel, J., Fung, G.: SVM feature selection for classification of SPECT images of Alzheimer’s disease using spatial information. In: Proceedings of the 5th IEEE International Conference on Data Mining (ICDM’05). IEEE, 8 p (2005)
Abdulkadir A, Mortamet B, Vemuri P, Jack Jr C.R., Krueger G, Klöppel, S.: Alzheimer’s disease neuroimaging initiative. Effects of hardware heterogeneity on the performance of SVM Alzheimer’s disease classifier. Neuroimage 58(3), 785–792 (2011)
Moller, C., Pijnenburg, Y.A., van der Flier, W.M., Versteeg, A., Tijms, B., de Munck, J.C., Hafkemeijer, A., Rombouts, S.A., van der Grond, J., van Swieten, J. Dopper, E., et al.: Alzheimer disease and behavioral variant frontotemporal dementia: automatic classification based on cortical atrophy for single-subject diagnosis. Radiology 279(3), 838–848 (2015)
Fulton, L.V., Dolezel, D., Harrop, J., Yan, Y., Fulton, C.P.: Classification of Alzheimer’s disease with and without imagery using gradient boosted machines and ResNet-50. Brain Sci. 9(9), 212 (2019)
Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., et al.: Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2014)
Sørensen, L., Igel, C., Pai, A., Balas, I., Anker, C., Lillholm, M., et al.: Differential diagnosis of mild cognitive impairment and Alzheimer’s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry. NeuroImage: Clinical 13, 470–482 (2017)
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Sharen, H., Dhanush, B., Rukmani, P., Dhanya, D. (2022). Efficient Diagnosis of Alzheimer’s Disease Using EfficientNet in Neuroimaging. In: Shaw, R.N., Das, S., Piuri, V., Bianchini, M. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Electrical Engineering, vol 914. Springer, Singapore. https://doi.org/10.1007/978-981-19-2980-9_18
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DOI: https://doi.org/10.1007/978-981-19-2980-9_18
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