Alzheimer’s Disease Detection with Deep Learning Architectures Designed Using Special Block Structures

Year 2023, Volume: 35 Issue: 2, 745 - 752, 01.09.2023

Eyup Hanbay Ali Arı

https://doi.org/10.35234/fumbd.1313523

Abstract

Alzheimer's disease, a type of dementia, is quite common in the world. The disease has different stages and there is still no cure. With current machine learning methods, different stages of the disease can be detected. Especially with deep learning-based methods, disease detection can be made sensitively. In this study, two different deep learning architectures have developed by using special block structures of ResNet and Inception architectures. These architectures have produced effective results in the detection of Alzheimer's. With the design of special block structures, the ability of different architectures to work together has been revealed. In the experimental results, it is seen that the proposed architectures produce effective results.

Keywords

Alzheimers’s disease detection, Deep learning, Inception, ResNet

Özel Blok Yapıları Kullanarak Tasarlanan Derin Öğrenme Mimarileri ile Alzheimer Hastalık Tespiti

Abstract

Bir demans türü olan Alzheimer hastalığı dünyada oldukça yaygın bir şekilde görülmektedir. Hastalığın farklı evreleri olup halen geçerli bir tedavisi yoktur. Güncel makine öğrenmesi yöntemleri ile hastalığın farklı evreleri tespit edilebilmektedir. Özellikle derin öğrenme tabanlı yöntemler ile hassas şekilde hastalık tespiti yapılabilmektedir. Bu çalışmada ResNet ve Inception mimarilerinin özel blok yapıları kullanılarak iki farklı derin öğrenme mimarisi geliştirilmiştir. Bu mimariler Alzheimer tespitinde etkin sonuçlar üretmiştir. Özel blok yapılarının tasarımı ile farklı mimarilerin birlikte çalışma yetenekleri ortaya çıkarılmıştır. Yapılan deneysel sonuçlarda önerilen mimarilerin etkin sonuçlar ürettiği görülmüştür.

Keywords

Alzheimer hastalığı tespiti, Derin öğrenme, Inception, ResNet

References

• Kumar A, Sidhu J, Goyal A, Tsao JW. Alzheimer Disease, StatPearls Publ, 2018; 1–27.
• Cheung, CY, Ran, AR, Wang S, Chan, VTT, Sham K, Hilal S, Venketasubramanian N, Cheng CY, ve diğerleri. A deep learning model for detection of Alzheimer’s disease based on retinal photographs: a retrospective, multicentre case-control study. The Lancet Digital Health 2022; 4(11): 806–815.
• Ari A, Alpaslan N, Hanbay D. Beyin MR görüntülerinden bilgisayar destekli tümör teşhisi sistemi. Med Technol Natl Conf; 15-18 Ekim 2015; Muğla, Türkiye. 1-4.
• Liu S, Masurkar AV, Rusinek H, Chen J, Zhang B, Zhu W, Fernandez-Granda C, Razavian N. Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs. Sci Rep 2022; 12(1): 1–12.
• Sathish K. L, Hariharasitaraman S, Narayanasamy K, Thinakaran K, Mahalakshmi J, Pandimurugan V. AlexNet approach for early stage Alzheimer’s disease detection from MRI brain images. Mater Today Proc 2021; 51: 58–65.
• Bai T, Du M, Zhang L, Ren L, Ruan L, Yang Y, Qian G, Meng Z, ve diğerleri. A novel Alzheimer’s disease detection approach using GAN-based brain slice image enhancement. Neural Comput. 2022; 492: 353–369.
• Shu F, Tian L. Deep Learning Methods for Alzheimer’s Disease Prediction. In CS230: Deep Learn, 2018, Stanford University. 1-10.
• Kong Z, Zhang M, Zhu W, Yi Y, Wang T, Zhang B. Multi-modal data Alzheimer’s disease detection based on 3D convolution. Biomed. Signal Process Control 2022; 75: 1-8.
• Ahmed S, Choi KY, Lee JJ, Kim BC, Kwon GR, Lee KH, Jung HY. Ensembles of Patch-Based Classifiers for Diagnosis of Alzheimer Diseases. IEEE Access 2019; 7: 73373–73383.
• Dubey S. "Alzheimer’s Dataset four class of Images" https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images, 2020.
• Sharma S, Gupta S, Gupta D, Altameem A, Saudagar AKJ, Poonia RC, Nayak SR. HTLML: Hybrid AI Based Model for Detection of Alzheimer’s Disease. Diagn 2022; 12(8): 1-16.
• Sharma S, Gupta S, Gupta D, Juneja S, Mahmoud A, El–Sappagh S, Kwak KS. Transfer learning-based modified inception model for the diagnosis of Alzheimer’s disease. Front. Comput. Neurosci. 2022; 16:1-13.
• Zena JI, Lucky E, Ellaine CG, Edbert IS, Suhartono D. Deep Learning Approach based Classification of Alzheimer’s Disease Using Brain MRI. 5th Int. Semin. Res of Inf Technol Intell Syst (ISRITI); 08-09 December 2022; Yogyakarta, Indonesia. 397–402.
• Singh P, Mishra SK. (2022). Alzheimer’s detection and categorization using a deep-learning approach. 3rd Int Conf on Intell Comput, Instrum Control Technol: Comput Intell for Smart Syst. ICICICT; 11-12 August 2022; Kerala India. 727–734.
• Toğaçar M, Cömert Z, Ergen B. Enhancing of dataset using DeepDream, fuzzy color image enhancement and hypercolumn techniques to detection of the Alzheimer’s disease stages by deep learning model. Neural Comput Appl 2021; 33(16): 9877–9889.
• Liu Y, Tang K, Cai W, Chen A, Zhou G, Li L, Liu R. MPC-STANet: alzheimer’s disease recognition method based on multiple phantom convolution and spatial transformation attention mechanism. Front Aging Neurosci 2022; 14: 1-15.
• He K, Zhang X, Ren, S, Sun, J. Deep residual learning for image recognition. Proceedings of the IEEE Comput Soc Conf Comput Vision Pattern Recognit; 2016; Las Vegas, NV, USA. 770–778.
• Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Comput Soc Conf Comput Vision and Pattern Recognit; 2016; Las Vegas, NV, USA. 2818–2826.
• Üzen H, Türkoğlu M, Arı A, Hanbay D. Piksel seviyesinde yüzey hata tespiti için InceptionV3 tabanlı zenginleştirilmiş öznitelik entegrasyon ağ mimarisi. J Faculty Eng Architect Gazi Univ 2022; 38(2): 721–732.
• Rao Y, He L, Zhu J. A residual convolutional neural network for pan-shaprening. Int. Workshop on Remote Sens Intell Process; 18-21 May 2017; Shanghai, China. 1-4.
• Simonyan K, Zisserman A. Very deep convolutional networks for large-scale ımage recognition. 3rd Int Conf Learn Representations; 2015; 1-14.
• Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Proceedings of the IEEE Comput Soc Conf Comput Vision and Pattern Recognit; 2017; Honolulu, Hawaii: 1-9.