Abstract
The human brain serves as the primary control centre for the humanoid system. Computer vision plays a vital part in the field of human health, which helps to reduce the amount of human judgement that is required to produce accurate findings. Scans using computed tomography, X-rays, and magnetic resonance imaging (MRI) are the most popular imaging technologies used in MRI, and they could also the greatest trustworthy and safe. The MRI can identify even the most minute of objects. In this paper, Alzheimer’s disease detection in early stage, based on MRI by using the deep learning technique U-Net and EfficientNet which is a convolutional neural network, is implemented. Diagnosing Alzheimer’s disease (AD) accurately is an vital aspect in treating AD patients, eventually during the early disease stages. This is particularly true in the early disease stages of the disease, when awareness of risk enables AD patients to take up protective measures well before the occurrence of brain damage that cannot be reversed. Despite of the fact that computers have been utilised in a significant number of recent research to diagnose AD, the majority of machine detection approaches are restricted by congenital findings. Early-stage Alzheimer’s disease (AD) can be identified, but early-stage AD cannot be predicted because prediction of the disease is successful only before the (AD) disease reveals itself. Deep learning, often known as DL, has recently emerged as a popular method for the initial recognition of Alzheimer’s disease (AD). In this article, we will give a quick overview of some of the key research that has been done on AD, and we will investigate how DL can assist researchers in the early phases of disease diagnosis.
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References
Aslam A, Khan E, Beg MMS (2015) Improved edge detection algorithm for brain tumor segmentation. Proced Comput Sci 58:430–437
Devkota B, Alsadoon A, Prasad PWC, Singh AK, Elchouemi A (2018) Image segmentation for early stage brain tumor detection using the a mathematical morphological reconstruction. Proced Comput Sci 125:115–123. https://doi.org/10.1016/j.procs.2017.12.017
Kaur J, Agrawal S, Renu V (2012) A comparative analysis of thresholding and edge detection segmentation techniques. Int J Comput Appl 39:29–34. https://doi.org/10.5120/4898-7432
Kumar M, Mehta KK (2011) A texture based tumor detection and automatic segmentation using seeded region growing method. Int J Comput Technol Appl 2(4):855–859
Mahmoud D, Mohamed E (2012) Brain tumor detection usingartificial neural networks. J Sci Technol 13:31–39
Marroquin JL, Vemuri BC, Botello S, Calderon F (2002) An accurate and efficientbayesian method for automatic segmentation of brain MRI. In:A, SparrG, Nielsen M, Johansen P (eds) Computer vision—ECCV 2002. ECCV 2002.Lecture notes in computer science, vol 2353. Springer, Berlin, Heidelberg
Sathya B, Manavalan R (2011) Image segmentation by clustering methods: performance analysis. Int J Comput Appl 29(11):27
Sekhar BVDS, et al (2019) Image denoising using novel social grouping optimization algorithm with transform domain technique. Int J Nat Comput Res 8(4):28–40
Sekhar BVDS, et al (2022a) Artificial neural network-based secured communication strategy for vehicular ad hoc network. Soft Comput 27(1):297–309
Sekhar BVDS, et al (2022b) Novel technique of threshold distance-based vehicle tracking system for woman safety. Intelligent system design, Lecture notes in networks and systems 494, India
Sekhar BVDS, et al (2023) Sustainable and reliable healthcare automation and digitization using deep learning technologies. J Sci Ind Res 23:226–231
Sekhar BVDS, Reddy PP, Varma G (2015a) Performance of secure and robust watermarking using evolutionary computing technique. JGIM 25(4):61–79
Sekhar BVDS, Reddy PP, Varma G (2015b) Novel technique of image denoising using adaptive haar wavelet transformation. Irecos 10(10):1012–1017
Sekhar BVDS, et al (2018) Image denoising using wavelet transform based flower pollination algorithm. Advs Intell Syst, Comput (Aisc), vol 862, Springer
Sivaramakrishnan KM (2013) A novel based approach for extraction of brain tumor in mri images using soft computing techniques. Int J Adv Res Comput Commun Eng 2(4):1845
Sudharani K, Sarma TC, Satya Rasad K (2015) Intelligent Brain Tumor lesion classification and identification from MRI images using k-NN technique. In: 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT),Kumaracoil, pp 777–780
Udayaraju P, Jeyanthi P (2022) Early diagnosis of age-related macular degeneration (ARMD) using deep learning. Smart Innov, Syst Technol 289:657–663
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Sekhar, B.V.D.S., Jagadev, A.K. Efficient Alzheimer’s disease detection using deep learning technique. Soft Comput 27, 9143–9150 (2023). https://doi.org/10.1007/s00500-023-08434-z
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DOI: https://doi.org/10.1007/s00500-023-08434-z