Diagnosis of Alzheimer's Disease by Three-Dimensional Convolutional Neural Network Using Unsupervised Feature Learning Method

Soliman, Sarah A.; El‐Dahshan, El‐Sayed A.; Salem, Abdel-Badeeh M.

doi:10.21608/ijicis.2021.80596.1103

Cited by 1 publication

(1 citation statement)

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“…The authors of [10] propose a 3D-CNN based on an upgraded sparse autoencoder for Alzheimer's disease prediction. The 3D-CNN was used to produce predictions based on the learnt records, and the sparse autoencoder was used to learn the best data representation.…”

Section: Related Workmentioning

confidence: 99%

Uncovering the Effects of Data Variation on Protein Sequence Classification Using Deep Learning

Mostafa

Afify

Ismail

et al. 2022

IJICIS

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Bioinformaticians face an issue in analyzing and studying protein similarity as the number of proteins grows. Protein sequence analysis helps in the prediction of protein functions. It is critical for the analysis process to be able to appropriately categorize proteins based on their sequences. The extraction of features from protein sequences is done using a variety of methods. The goal of this study is to investigate the different variations of data on the classification performance of a deep learning model employing 3D data. First, few research questions were formulated regarding the impact of the following criteria: dataset size, IMF importance, feature size, and preprocessing on the proposed deep learning classification process. Second, comprehensive experiments were conducted to answer the research questions. Six feature extraction methods were utilized to create 3D features with two sizes (7x7x7 and 9x9x9), which were then fed into a convolutional neural network. Three datasets different in their sorts, sizes, and balance state were used. Accuracy, precision, recall and F1-score are the standard assessment metrics used. Experimental results draw significant conclusions. First, the 7x7x7 feature matrix has a positive correlation between its dimensions, which improved the results. Second, using the sum of the first three IMF components had better impact than using the first IMF component. Third, the classification process did not benefit from the normalization of features for small datasets unlike the large dataset. Finally, the dataset size had a significant impact on training the CNN model, with a training accuracy reaching 84.03%.

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Section: Related Workmentioning

confidence: 99%