A Bio-inspired Convolution Neural Network Architecture For Automatic Breast Cancer Detection And Classification Using Rna-seq Gene Expression Data

This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process. Filtration is used for gene reduction using a threshold value of 0.25. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA). The proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, and an f1-score.