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@article{159908,
        author = {ROSHINI S P and Dr. T. Thenmozhi},
        title = {Classification of Alzheimer’s disease from Brain MRI Using Transfer Learning From CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {12},
        pages = {871-875},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159908},
        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.},
        keywords = {CNN, MRI, generic feature, PCA, TSNE, Classifier},
        month = {},
        }