Classification of Alzheimerâ€™s disease from Brain MRI Using Transfer Learning From CNN

Q: How many days will it take for my paper to be published?

The review time for papers is not fixed. However, if the paper is accepted and the author completes the processing charges formalities, the paper will be published within a few working days.

Q: I would like to receive a hard copy of the journal materials. Are there any additional charges?

You can log in to the author portal and pay 500 INR to receive the hard copy materials.

ROSHINI S P; Dr. T. Thenmozhi

Classification of Alzheimerâ€™s disease from Brain MRI Using Transfer Learning From CNN

Authors: ROSHINI S P, Dr. T. Thenmozhi

Unique Paper ID: 159908
Volume: 9
Issue: 12
PageNo: 871-875

Keywords: CNN MRI generic feature PCA TSNE Classifier

Abstract:
For picture categorization and object recognition, various Convolutional Neural Network (CNN) architectures have been developed. It is a challenging effort for CNN to cope with hundreds of MRI Image slices, each of almost similar nature in a single patient, for the image-based categorization. As a result, employing 2D CNN architecture to categorise a number of patients as AD, MCI, or NC based on 3D MRI becomes a hazy technique. As a result, we have simplified the concept of classifying patients based on 3D MRI while acknowledging the 2D features produced by the CNN framework in order to overcome this issue. We outline our approach for extracting 2D characteristics from an MRI and transforming them so that they can be used in a machine learning algorithm for classification. Our study's findings are displayed of classifying patients into 3 groups. We used pretrained Alexnet CNN or scratched trained CNN as a general feature extractor for 2D images, whose dimensions were decreased using PCA+TSNE. Finally, we classified the images using a straightforward machine learning technique like KNN. Even while the outcome is not particularly stunning, it clearly demonstrates that it can be superior to CNN softmax classification trained from scratch using probability scores. The created feature is easily manipulable and can be enhanced for greater specificity, sensitivity, and accuracy.

Download article

email to a friend

Cite This Article

ISSN: 2349-6002
Volume: 9
Issue: 12
PageNo: 871-875

Classification of Alzheimerâ€™s disease from Brain MRI Using Transfer Learning From CNN

Available:https://ijirt.org/article?manuscript=159908

23 Mar, 2023

NEXT-GENERATION COMMUNICATION SYSTEMS: X2-BASED SIGNALLING TECHNIQUES FOR DOWNLINK UPLINK DECOUPLING

15 Dec, 2022

DESIGN OF MICROSTRIP PATCH ANTENNA FOR MEDICAL APPLICATION USING WBAN

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now