Naglaa. F. Soliman1,*, Samia M. Abd-Alhalem2
, Walid El-Shafai2
, Salah Eldin S. E. Abdulrahman3,
N. Ismaiel3
, El-Sayed M. El-Rabaie2
, Abeer D. Algarni1, Fathi E. Abd El-Samie1,2
CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5907-5927, 2022, DOI:10.32604/cmc.2022.018860
- 11 October 2021
Abstract
Recently, deep learning (DL) became one of the essential tools in bioinformatics. A modified convolutional neural network (CNN) is employed in this paper for building an integrated model for deoxyribonucleic acid (DNA) classification. In any CNN model, convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features. A novel method based on downsampling and CNNs is introduced for feature reduction. The downsampling is an improved form of the existing pooling layer to obtain better classification accuracy. The two-dimensional discrete transform (2D DT) and two-dimensional random projection (2D RP) methods
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