Comparative Analysis of Deep Learning in Detecting Cognitive Impairment Associated with Alzheimer's Disease
Keywords:
Alzheimer's Disease, MRI scans, Machine Learning, Cognitive Impairment, Ensemble ApproachAbstract
This study studies the usefulness of machine learning patterns within the early identification of cognitive impairment connected to Alzheimer's Disease the use of MRI images. A diverse dataset containing 2330 images from hospital and on-line resources provides the foundation of our observation. Four fantastic fashions—Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), K-Nearest Neighbors (KNN), and the VGG16 structure—are trained and investigated. Image preparation procedures, inclusive of normalization, cropping and resizing, image augmentation, feature extraction, and statistics augmentation, are routinely performed to enhance the dataset. After undergoing extensive training, the CNN becomes the best acting model, with a 95.78% accuracy rate. The accuracy of KNN, RNN, and VGG16 is 93.5 percent, 91.9 percent, and 876 percent, respectively. The confusion matrices illuminate the subtle performance of every edition, offering insights into their skills to effectively distinguish amazing and bad occasions. The ensemble technique, utilising the complementing qualities of several fashions, gives a complete understanding of cognitive impairment. Our results contribute contributions to the growing field of machine mastering packages in scientific imaging, highlighting the significance of a holistic study for improved diagnostic accuracy. Our research represents an important step towards more potent diagnostic tools as the field develops, providing insights that go beyond the specific models used and have implications for advanced affected person outcomes in the field of Alzheimer's Disease and related neurodegenerative issues.
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D. Ashourloo, H. Aghighi, A. A. Matkan, M. R. Mobasheri, and A. M. Rad, “An Investigation Into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 9, pp. 4344–4351, 2016, doi: 10.1109/JSTARS.2016.2575360.
S. Akter, M. Amina, and N. Mansoor, “Early Diagnosis and Comparative Analysis of Different Machine Learning Algorithms for Myocardial Infarction Prediction,” IEEE Region 10 Humanitarian Technology Conference, R10-HTC, vol. 2021-Septe, 2021, doi: 10.1109/R10-HTC53172.2021.9641080.
F. Jiang, Y. Lu, Y. Chen, D. Cai, and G. Li, “Image recognition of four rice leaf diseases based on deep learning and support vector machine,” Computers and Electronics in Agriculture, vol. 179, no. August, p. 105824, 2020, doi: 10.1016/j.compag.2020.105824.
D. Swain, S. K. Pani, and D. Swain, “A Metaphoric Investigation on Prediction of Heart Disease using Machine Learning,” 2018 International Conference on Advanced Computation and Telecommunication, ICACAT 2018, 2018, doi: 10.1109/ICACAT.2018.8933603.
S. M. Javidan, A. Banakar, K. A. Vakilian, and Y. Ampatzidis, “Diagnosis of Grape Leaf Diseases Using Automatic K-Means Clustering and Machine Learning,” SSRN Electronic Journal, vol. 3, no. March 2022, 2022, doi: 10.2139/ssrn.4062708.
A. Pandiaraj, S. L. Prakash, and P. R. Kanna, “Effective heart disease prediction using hybridmachine learning,” Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, no. Icicv, pp. 731–738, 2021, doi: 10.1109/ICICV50876.2021.9388635.
P. Dikshit, B. Dey, A. Shukla, A. Singh, T. Chadha, and V. K. Sehgal, “Prediction of Breast Cancer using Machine Learning Techniques,” ACM International Conference Proceeding Series, pp. 382–387, 2022, doi: 10.1145/3549206.3549274.
A. S. Zamani et al., “Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection,” Journal of Food Quality, vol. 2022, pp. 1–7, 2022, doi: 10.1155/2022/1598796.
R. Qasrawi, M. Amro, R. Zaghal, M. Sawafteh, and S. V. Polo, “Machine Learning Techniques for Tomato Plant Diseases Clustering, Prediction and Classification,” Proceedings - 2021 International Conference on Promising Electronic Technologies, ICPET 2021, pp. 40–45, 2021, doi: 10.1109/ICPET53277.2021.00014.
S. Tuli, S. Tuli, R. Tuli, and S. S. Gill, “Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing,” Internet of Things (Netherlands), vol. 11, 2020, doi: 10.1016/j.iot.2020.100222.
Y. Muhammad, M. D. Alshehri, W. M. Alenazy, T. Vinh Hoang, and R. Alturki, “Identification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques,” Mobile Information Systems, vol. 2021, 2021, doi: 10.1155/2021/9989237.
A. Gahane and C. Kotadi, “An Analytical Review of Heart Failure Detection based on IoT and Machine Learning,” Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022, pp. 1308–1314, 2022, doi: 10.1109/ICAIS53314.2022.9742913.
Neelakantan. P, “Analyzing the best machine learning algorithm for plant disease classification,” Materials Today: Proceedings, no. xxxx, 2022, doi: 10.1016/j.matpr.2021.07.358.
F. Hao and K. Zheng, “Online Disease Identification and Diagnosis and Treatment Based on Machine Learning Technology,” Journal of Healthcare Engineering, vol. 2022, 2022, doi: 10.1155/2022/6736249.
R. Kumar, A. Chug, A. P. Singh, and D. Singh, “A Systematic Analysis of Machine Learning and Deep Learning Based Approaches for Plant Leaf Disease Classification: A Review,” Journal of Sensors, vol. 2022, 2022, doi: 10.1155/2022/3287561.
M. Gomez Selvaraj et al., “Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 169, no. April, pp. 110–124, 2020, doi: 10.1016/j.isprsjprs.2020.08.025.
P. S. Thakur, P. Khanna, T. Sheorey, and A. Ojha, “Trends in vision-based machine learning techniques for plant disease identification: A systematic review,” Expert Systems with Applications, vol. 208, no. July, p. 118117, 2022, doi: 10.1016/j.eswa.2022.118117.
G. Shial, S. Sahoo, and S. Panigrahi, “Identification and Analysis of Breast Cancer Disease using Swarm and Evolutionary Algorithm,” 2022 IEEE Region 10 Symposium, TENSYMP 2022, pp. 1–6, 2022, doi: 10.1109/TENSYMP54529.2022.9864514.
A. Bah and M. Davud, “Analysis of Breast Cancer Classification with Machine Learning based Algorithms,” 2022 2nd International Conference on Computing and Machine Intelligence, ICMI 2022 - Proceedings, 2022, doi: 10.1109/ICMI55296.2022.9873696.
H. Sami, M. Sagheer, K. Riaz, M. Q. Mehmood, and M. Zubair, “Machine Learning-Based Approaches for Breast Cancer Detection in Microwave Imaging,” 2021 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2021 - Proceedings, pp. 72–73, 2021, doi: 10.23919/USNC-URSI51813.2021.9703518.
D. P. Singh and B. Kaushik, “Machine learning concepts and its applications for prediction of diseases based on drug behaviour: An extensive review,” Chemometrics and Intelligent Laboratory Systems, vol. 229, no. August, p. 104637, 2022, doi: 10.1016/j.chemolab.2022.104637.
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