RESULTS: After representing human exons and introns with color images using Frequency Chaos Game Representation, two pre-trained convolutional neural network models (Resnet-50 and GoogleNet) and a proposed CNN model having 13 hidden layers were used to classify our obtained images. We have reached a value of 92% for the accuracy rate for Resnet-50 model in about 7 h for the execution time, a value of 91.5% for the accuracy rate for the GoogleNet model in 2 h and a half for the execution time. For our proposed CNN model, we have reached 91.6% for the accuracy rate in 2 h and 37 min.
CONCLUSIONS: Our proposed CNN model is faster than the Resnet-50 model in terms of execution time. It was able to slightly exceed the GoogleNet model for the accuracy rate value.
结果:使用频率混沌游戏表示用彩色图像表示人类外显子和内含子后,使用两个预训练的卷积神经网络模型(Resnet-50和GoogleNet)和一个建议的具有13个隐藏层的CNN模型对我们获得的图像进行分类。我们已经达到了92%的值的准确率为Resnet-50模型在大约7小时的执行时间,GoogleNet模型在2小时半的执行时间内的准确率值为91.5%。对于我们提出的CNN模型,在2h和37min内,我们的准确率达到91.6%。
结论:我们提出的CNN模型在执行时间方面比Resnet-50模型更快。它能够稍微超过GoogleNet模型的准确率值。