Early Detection of Dementia using Deep Learning and Image Processing

Автор: Basavaraj Mali Patil, Megha Rani Raigonda, Sudhir Anakal, Ambresh Bhadrashetty

Журнал: International Journal of Engineering and Manufacturing @ijem

Статья в выпуске: 1 vol.13, 2023 года.

Бесплатный доступ

Dementia is the world's most deadly disease. A degenerative disorder that affects the thinking, memory, and communication abilities of the human brain. According to World Health Organization, more than 40 million people worldwide suffer from this illness. One of the most common methods for analyzing the human brain, including detecting dementia, is using MRI (Magnetic resonance imaging) data, which provides insight into the inner working of the human body. Using MRI images a deep Convolution neural network was designed to detect dementia, we are utilizing image processing to help doctors detect diseases and make decisions on observation, in an earlier stage of the disease. In this paper, we are going to get to the bottom of the DenseNet-169 model, to detect Dementia. There are approximately 6000 brain MRI images in the database for which the DenseNet-169 model has been used for classification purposes. It is a Convolution Neural Network (CNN) model that classifies Non-Dementia, Mild Dementia, Severe Dementia, and Moderate Dementia. The denseNet-169 model helps us determine Dementia disease. And also present the 97% accuracy for clarification of disease is present in the patient body. we are conducted this survey for providing effective disease prediction model for physicians to conclude that the disease stage is accurate and provide proper treatment for that.

Еще

DenseNet-169, Dementia, Brain images, Neural Network, Magnetic Resonance image

Короткий адрес: https://sciup.org/15018605

IDR: 15018605   |   DOI: 10.5815/ijem.2023.01.02

Список литературы Early Detection of Dementia using Deep Learning and Image Processing

  • Epidemiology, “study and analysis of the distribution, patterns and determinants of health and disease conditions in defined population.”September 2021.
  • Wang, Shuihua& Phillips, Preetha& Sui, Yuxiu& Liu, Bin & Yang, Ming & Cheng, Hong. (2018). Classification of Alzheimer’s Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling. Journal of Medical Systems. 42. 10.1007/s10916-018-0932-7.
  • S. Sarraf and G. Tofighi, "Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data," 2016 Future Technologies Conference (FTC), 2016, pp. 816-820, doi: 10.1109/FTC.2016.7821697.
  • Ammarah Farooq, SyedMuhammad Anwar, Muhammad Awais, and Saad Rehman. 2017. A deep CNN based multi-class classification of Alzheimer's disease using MRI. In 2017 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE Press, 1–6. https://doi.org/10.1109/IST.2017.8261460.
  • A. Nawaz, S. M. Anwar, R. Liaqat, J. Iqbal, U. Bagci and M. Majid, "Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI Data," 2020 IEEE 23rd International Multitopic Conference (INMIC), 2020, pp. 1-6, doi: 10.1109/INMIC50486.2020.9318172.
  • F.Previtali, P. Bertolazzi, G. Felici, E. Weitschek, A novel method and software for automatically classifying Alzheimer’s disease patients by magnetic resonance imaging analysis, Computer Methods and Programs in Biomedicine,Volume 143,2017,Pages 89-95,ISSN 0169-2607,https://doi.org/10.1016/j.cmpb.2017.03.006.
  • Jo Taeho, NhoKwangsik, Saykin Andrew J, Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data,Frontiers in Aging Neuroscience,VOLUME11,2019,https://www.frontiersin.org/articles/10.3389/fnagi.2019.00220, DOI=10.3389/fnagi.2019.00220,ISSN=1663-4365.
  • Raigonda, Megha Rani, Sujatha P Terdal, and Baswaraj Raigond. 2022. “Detection of the Viral Disease on the Potato Foliar and Tubers Using a Machine Learning Approach”. International Journal of Health Sciences 6 (S4):9336-54
  • S. Liu, S. Liu, W. Cai, S. Pujol, R. Kikinis, and D. Feng, “Early diagnosis of Alzheimer’s disease with deep learning.”, IEEE 11th Int. Symp. Biomed. Imaging, ISBI 2014.
  • Folstein, M.F., Folstein, S. E., & McHugh, P. R. (1957) “Mini-mental state A practical method for grading the cognitive state of patients for Theclinician”, Journal of Psychiatric Research, 12: 189-198.
  • Buckner, R. L., Head, D., Parker, J., Fotenos, A. F., Marcus, D., Morris, J. C., et al. (2004)“A unified approach for morphometric and Functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: Reliability and validation.
  • Asmiya Naikodi ,Nida Fatima, Shamili.P,Neha Gopal.N ”Early detection of Alzheimer’s Disease using image processing using MRI Scan ”.International Journal for Technological Research in Engineering, Volume 3 , Issue 9 ,May 2016.ISSN:2347-4718.
  • Rohini Paul Joseph, C.Senthil Singh, M.Manikandan.”Brain Tumor MRI Image Segmentation and Detection in Image Processing”. International Journal of Research in Engineering and Techonology.eISSN:2319-1163|PISSN:2321-7308.
  • John Martin, Alex Pentland, Member IEEE Computer Society, Stan Sclaroff Member IEEE Computer Society and Ron Kikinis. “Characterization of Neuropathology Shape Deformations”.
  • Raigonda, Megha Rani, Sujatha P Terdal, and Baswaraj Raigond. 2022. “Detection of the Viral Disease on the Potato Foliar and Tubers Using a Machine Learning Approach”. International Journal of Health Sciences 6 (S4):9336-54
  • Sudhir Anakal, P Sandhya, "Decision Support System for Drug-Drug Interaction Pertaining to COPD and its Comorbidities", International Journal of Education and Management Engineering (IJEME), Vol.12, No.2, pp. 1-6, 2022. DOI: 10.5815/ijeme.2022.02.01
Еще
Статья научная