The normal and cancer cell tissues exhibit different gene expressions. Therefore, gene expression data are the effective source for cancer classification, in which the usage of the original gene expression data is challenging due to their high dimension and small size of the data samples. This article proposes a fractional biogeography-based optimization-based deep convolutional neural network (FBBO-based deep CNN) for cancer classification. The developed FBBO is the integration of the fractional calculus (FC) in the biogeography-based optimization (BBO), which aims at determining the optimal weights for tuning the deep CNN. Initially, the gene expression data is pre-processed and subjected to dimensional reduction using the probabilistic principal component analysis (PPCA). The selected features are used for cancer classification enabling a high degree of robustness and accuracy. The experimental analysis using the Colon dataset and Leukemia dataset reveals that the proposed classifier acquired maximal accuracy, sensitivity, specificity, precision, and F-Measure of 0.98.

中文翻译：

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一种使用优化驱动的深度学习进行癌症分类的方法

正常和癌细胞组织表现出不同的基因表达。因此，基因表达数据是癌症分类的有效来源，其中原始基因表达数据由于数据样本的高维度和小规模而具有挑战性。本文提出了一种基于分数生物地理学优化的深度卷积神经网络（FBBO-based deep CNN）用于癌症分类。开发的 FBBO 是分数阶微积分 (FC) 在基于生物地理学的优化 (BBO) 中的集成，旨在确定用于调整深度 CNN 的最佳权重。最初，使用概率主成分分析 (PPCA) 对基因表达数据进行预处理和降维。选定的特征用于癌症分类，从而实现高度的稳健性和准确性。使用 Colon 数据集和 Leukemia 数据集的实验分析表明，所提出的分类器获得了最大的准确度、灵敏度、特异性、精确度和 0.98 的 F-Measure。