RNN-CNN Based Cancer Prediction Model for Gene Expression

Abstract

One of those illnesses that is most deadly to people is cancer. The only way to prevent any harm to humanity is by its early discovery and treatment. Various types of tests are conducted in the medical labs for the detection of cancer. Cancer can also be detected at the genetic level. For this distinct machine learning and deep learning methods already exist. This paper proposes a hybrid method based on Recurrent Neural Network (RNN) and Convolution Neural Network (CNN) to predict different types of cancer such as Breast, Lung, Uterine, Kidney, Prostate and colon cancer from gene expression data. The bottleneck features are extracted using the sandwich stacked method based on VGG16 and VGG19 pre-trained models. Afterward, the proposed hybrid classifier based on RNN-CNN has been used to classify the data into various classes. The proposed model performs better than the other existing methods such as VGG16, VGG19, ResNet50, Inception V3 and MobileNet classifier in terms of various performance metrics such as accuracy, Mean Square Error (MSE), precision, recall, and F1 score. RNN-CNN classifier provides the highest accuracy of 0.978 among all the other existing methods for Dataset 1 and the highest accuracy of 0.994 for Dataset 2 at 80% training data. On the other hand, RNN-CNN classifier provides the lowest MSE of 0.101 among all the other existing methods for Dataset 1 and the lowest MSE of 0.006 for Dataset 2 at 80% training data.