Jaddi Najmeh Sadat, Abdullah Salwani, Goh Say Leng, Hasan Mohammad Kamrul
Faculty of Computer Engineering Iranian eUniversity Tehran Iran.
Data Mining and Optimization Research Group (DMO) Centre for Artificial Intelligence Technology Faculty of Information Science and Technology National University of Malaysia Bangi Selangor Malaysia.
Healthc Technol Lett. 2024 Nov 22;11(6):485-495. doi: 10.1049/htl2.12097. eCollection 2024 Dec.
This paper develops a method for cancer classification from microRNA data using a convolutional neural network (CNN)-based model optimized by genetic algorithm. The convolutional neural network has performed well in various recognition and perception tasks. This paper contributes to the cancer classification using a union of two CNNs. The method's performance is boosted by the relationship between CNNs and exchanging knowledge between them. Besides, communication between small sizes of CNNs reduces the need for large size CNNs and, consequently, the computational time and memory usage while preserving high accuracy. The method proposed is tested on microRNA dataset containing the genomic information of 8129 patients for 29 different types of cancer with 1046 gene expression. The classification accuracy of the selected genes obtained by the proposed approach is compared with the accuracy of 22 well-known classifiers on a real-world dataset. The classification accuracy of each cancer type is also ranked with the results of 77 classifiers reported in previous works. The proposed approach shows accuracy of 100% in 24 out of 29 classes and in seven cases out of 29, the method achieved 100% accuracy that no classifier in other studies has reached. Performance analysis is performed using performance metrics.
本文提出了一种基于卷积神经网络（CNN）模型并通过遗传算法优化的方法，用于从微小RNA数据中进行癌症分类。卷积神经网络在各种识别和感知任务中表现出色。本文通过结合两个卷积神经网络对癌症分类做出了贡献。该方法的性能通过卷积神经网络之间的关系以及它们之间的知识交换得到提升。此外，小型卷积神经网络之间的通信减少了对大型卷积神经网络的需求，从而在保持高精度的同时减少了计算时间和内存使用。所提出的方法在包含8129名患者的29种不同类型癌症的1046个基因表达的微小RNA数据集上进行了测试。将所提出方法获得的所选基因的分类准确率与在真实世界数据集上22个知名分类器的准确率进行了比较。每种癌症类型的分类准确率也与先前研究报告的77个分类器的结果进行了排名。所提出的方法在29个类别中的24个类别中显示出100%的准确率，在29个案例中的7个案例中，该方法实现了100%的准确率，这是其他研究中的分类器所未达到的。使用性能指标进行了性能分析。
