APA Style
Nandiraju, S. K. K. , Nandiraju, S. K. K. Chundru, S. K. , Chundru, S. K. Vangala, S. R. , Vangala, S. R. Polam, R. M. , Polam, R. M. Kamarthapu, B. , & Kamarthapu, B. (2022). Advance of AI-Based Predictive Models for Diagnosis of Alzheimer's Disease (AD) in Healthcare.
Current Research in Public Health, 2(1), 141-152.
https://doi.org/10.31586/jaibd.2022.1340
ACS Style
Nandiraju, S. K. K. ; Nandiraju, S. K. K. Chundru, S. K. ; Chundru, S. K. Vangala, S. R. ; Vangala, S. R. Polam, R. M. ; Polam, R. M. Kamarthapu, B. ; Kamarthapu, B. Advance of AI-Based Predictive Models for Diagnosis of Alzheimer's Disease (AD) in Healthcare.
Current Research in Public Health 2022 2(1), 141-152.
https://doi.org/10.31586/jaibd.2022.1340
Chicago/Turabian Style
Nandiraju, Sri Krishna Kireeti, Sri Krishna Kireeti Nandiraju. Sandeep Kumar Chundru, Sandeep Kumar Chundru. Srikanth Reddy Vangala, Srikanth Reddy Vangala. Ram Mohan Polam, Ram Mohan Polam. Bhavana Kamarthapu, and Bhavana Kamarthapu. 2022. "Advance of AI-Based Predictive Models for Diagnosis of Alzheimer's Disease (AD) in Healthcare".
Current Research in Public Health 2, no. 1: 141-152.
https://doi.org/10.31586/jaibd.2022.1340
AMA Style
Nandiraju SKK, Nandiraju SKKChundru SK, Chundru SKVangala SR, Vangala SRPolam RM, Polam RMKamarthapu B, Kamarthapu B. Advance of AI-Based Predictive Models for Diagnosis of Alzheimer's Disease (AD) in Healthcare.
Current Research in Public Health. 2022; 2(1):141-152.
https://doi.org/10.31586/jaibd.2022.1340
@Article{crph1340,
AUTHOR = {Nandiraju, Sri Krishna Kireeti and Chundru, Sandeep Kumar and Vangala, Srikanth Reddy and Polam, Ram Mohan and Kamarthapu, Bhavana and Kakani, Ajay Babu},
TITLE = {Advance of AI-Based Predictive Models for Diagnosis of Alzheimer's Disease (AD) in Healthcare},
JOURNAL = {Current Research in Public Health},
VOLUME = {2},
YEAR = {2022},
NUMBER = {1},
PAGES = {141-152},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1340},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2022.1340},
ABSTRACT = {The effects on the elderly are disproportionately Alzheimer’s disease (AD) is one of the most prevalent and chronic types of dementia. Alzheimer's disease (AD), a fatal illness that can harm brain structures and cells long before symptoms appear, is currently incurable and incurable. Using brain MRI pictures from a publicly accessible Kaggle dataset, this study suggests a prediction model based on Convolutional Neural Networks (CNNs) to help with the early detection of Alzheimer's disease. Four levels of dementia have been applied to the 6,400 photos in the collection: not demented, slightly demented, moderately demented, and considerably mildly demented. Pixel normalization, class balancing utilizing data augmentation techniques, and picture scaling to 128×128 pixels were all part of a thorough workflow for data preparation. To improve the gathering of spatial dependence in volumetric MRI data, a 3D convolutional neural network (CNN) architecture was used. We used important performance measures including F1-score, recall, accuracy, precision, and log loss to gauge the model's effectiveness. A review of the available data indicates that the total F1-score, accuracy, recall, and precision were 99.0%, 99.0%, and 99.38%, respectively. The findings demonstrate the model's potential for practical use in early AD diagnosis and establish its robustness with the help of confusion matrix analysis and performance curves.},
}
%0 Journal Article
%A Nandiraju, Sri Krishna Kireeti
%A Chundru, Sandeep Kumar
%A Vangala, Srikanth Reddy
%A Polam, Ram Mohan
%A Kamarthapu, Bhavana
%A Kakani, Ajay Babu
%D 2022
%J Current Research in Public Health
%@ 2831-5162
%V 2
%N 1
%P 141-152
%T Advance of AI-Based Predictive Models for Diagnosis of Alzheimer's Disease (AD) in Healthcare
%M doi:10.31586/jaibd.2022.1340
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/1340
TY - JOUR
AU - Nandiraju, Sri Krishna Kireeti
AU - Chundru, Sandeep Kumar
AU - Vangala, Srikanth Reddy
AU - Polam, Ram Mohan
AU - Kamarthapu, Bhavana
AU - Kakani, Ajay Babu
TI - Advance of AI-Based Predictive Models for Diagnosis of Alzheimer's Disease (AD) in Healthcare
T2 - Current Research in Public Health
PY - 2022
VL - 2
IS - 1
SN - 2831-5162
SP - 141
EP - 152
UR - https://www.scipublications.com/journal/index.php/JAIBD/article/view/1340
AB - The effects on the elderly are disproportionately Alzheimer’s disease (AD) is one of the most prevalent and chronic types of dementia. Alzheimer's disease (AD), a fatal illness that can harm brain structures and cells long before symptoms appear, is currently incurable and incurable. Using brain MRI pictures from a publicly accessible Kaggle dataset, this study suggests a prediction model based on Convolutional Neural Networks (CNNs) to help with the early detection of Alzheimer's disease. Four levels of dementia have been applied to the 6,400 photos in the collection: not demented, slightly demented, moderately demented, and considerably mildly demented. Pixel normalization, class balancing utilizing data augmentation techniques, and picture scaling to 128×128 pixels were all part of a thorough workflow for data preparation. To improve the gathering of spatial dependence in volumetric MRI data, a 3D convolutional neural network (CNN) architecture was used. We used important performance measures including F1-score, recall, accuracy, precision, and log loss to gauge the model's effectiveness. A review of the available data indicates that the total F1-score, accuracy, recall, and precision were 99.0%, 99.0%, and 99.38%, respectively. The findings demonstrate the model's potential for practical use in early AD diagnosis and establish its robustness with the help of confusion matrix analysis and performance curves.
DO - Advance of AI-Based Predictive Models for Diagnosis of Alzheimer's Disease (AD) in Healthcare
TI - 10.31586/jaibd.2022.1340
ER -
