ICTACT Journal on Image and Video Processing

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Dementia Disease Classification With Rotation Forests Based DCGAN


Affiliations
1 Department of Computer Science and Engineering, School of Engineering and Technology, CMR University, India
2 Department of Computer Science and Engineering, P.A College of Engineering and Technology, India
3 Department of Computing and Engineering, University of West London, United Arab Emirates
4 Telus International, West Bengal, India
     

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This research paper introduces a novel approach for the classification of dementia disease using Rotation Forests based on Deep Convolutional Generative Adversarial Networks (DCGAN). Dementia is a significant cognitive disorder prevalent among the elderly population, demanding accurate and early diagnosis for effective intervention. Traditional methods often rely on manual feature extraction and shallow learning, which may lack the ability to capture intricate patterns in complex medical data. In this study, we propose a fusion of Rotation Forests, a robust ensemble learning technique, with DCGAN, a deep learning model recognized for its feature extraction capabilities. The Rotation Forests enhance the diversity of the base classifiers, while DCGAN learns meaningful features from raw medical imaging data. We validate the proposed approach on a comprehensive dataset and compare its performance against existing methods. The experimental results demonstrate the effectiveness of the Rotation Forests based on DCGAN approach in accurately classifying dementia diseases, showcasing its potential as a valuable tool in medical diagnosis.

Keywords

Dementia disease, Classification, Rotation Forests, Deep Convolutional Generative Adversarial Networks, Medical Imaging
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  • Dementia Disease Classification With Rotation Forests Based DCGAN

Abstract Views: 49  |  PDF Views: 1

Authors

K. Prabhakar
Department of Computer Science and Engineering, School of Engineering and Technology, CMR University, India
M. Umaselvi
Department of Computer Science and Engineering, P.A College of Engineering and Technology, India
Shibili Said
Department of Computing and Engineering, University of West London, United Arab Emirates
Saswata Das
Telus International, West Bengal, India

Abstract


This research paper introduces a novel approach for the classification of dementia disease using Rotation Forests based on Deep Convolutional Generative Adversarial Networks (DCGAN). Dementia is a significant cognitive disorder prevalent among the elderly population, demanding accurate and early diagnosis for effective intervention. Traditional methods often rely on manual feature extraction and shallow learning, which may lack the ability to capture intricate patterns in complex medical data. In this study, we propose a fusion of Rotation Forests, a robust ensemble learning technique, with DCGAN, a deep learning model recognized for its feature extraction capabilities. The Rotation Forests enhance the diversity of the base classifiers, while DCGAN learns meaningful features from raw medical imaging data. We validate the proposed approach on a comprehensive dataset and compare its performance against existing methods. The experimental results demonstrate the effectiveness of the Rotation Forests based on DCGAN approach in accurately classifying dementia diseases, showcasing its potential as a valuable tool in medical diagnosis.

Keywords


Dementia disease, Classification, Rotation Forests, Deep Convolutional Generative Adversarial Networks, Medical Imaging

References