Earticle

초록

영어

The best defenses against AD are early detection and treatment. Recent developments in magnetic resonance imaging (MRI)-based computer-aided diagnostics have demonstrated promise in precisely classifying AD. To investigate these methods, this study makes use of the OASIS MRI dataset, which consists of 80,000 brain MRI pictures. Resampling was required in order to prevent bias towards the majority class when convolutional neural network (CNN) models were applied for classification due to the large imbalance of the dataset. The image slices obtained from 3D OASIS dataset's were examined for this study utilizing the EfficientNetV2B0 with customized classification layers and a condensed custom CNN model. There are four categories: nondemented, very mild demented, mild demented, and moderately demented. We covered multiclass as well as binary classification. Using various dataset sizes, the study evaluated two models: EfficientNetV2B0 and a customized sequential CNN model, with 98% and 96% accuracy, respectively. The findings address the disparity in different class sizes and demonstrate the promise of sophisticated CNN architectures for Alzheimer's disease classification and early detection.

목차

Abstract
1. INTRODUCTION
2. MATERIALS AND METHODS
A. Data
B. Data Acquisition
C. Proposed Model
3. RESULT AND DISCUSSION
4. DISCUSSION
5. CONCLUSION
ACKNOWLEDGMENT
CONFLICT OF INTEREST
REFERENCES

저자

• Faizaan Fazal Khan [ Dept. of Information and Communication Engineering, Chosun University ]
• Ji-In Kim [ Dept. of Information and Communication Engineering, Chosun University ]
• Jae-Young Pyun [ Dept. of Information and Communication Engineering, Chosun University ]
• Goo-Rak Kwon [ Dept. of Information and Communication Engineering, Chosun University ]

참고문헌