CONVOLUTIONAL NEURAL NETWORK MODELS FOR CANCER TREATMENT RESPONSE PREDICTION | ||
| International Journal of Intelligent Computing and Information Sciences | ||
| Volume 23, Issue 1, March 2023, Pages 98-105 PDF (791.66 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijicis.2023.180508.1239 | ||
| Authors | ||
| Hanan Ahmed* 1; Howida Shedeed2; Safwat Hamad3; Ashraf Saad4 | ||
| 1Scientific Computing, Faculty of Computer and Information Sciences, Ains Shams University, Cairo, Egypt | ||
| 2Scientific Computing, Faculty of Computer and Information Sciences, Ains Shams University, Cairo, Egypt | ||
| 3Department of Scientific Computing, Faculty of Computers & Information Sciences, Ain Shams University. | ||
| 4Scientific Computing Department, Faculty of Computer and Information Sciences, Ains Shams University, Cairo, Egypt | ||
| Abstract | ||
| Recently, efforts are exerted on cancer treatment prediction based on the biomarkers related to the tumor. Gene expression and mutation profiles are the most used biomarkers for cancer prediction. Machine learning and deep learning algorithms have been used to predict drug response. The recent research show that the performance of deep learning models is better than the performance of machine learning based one. In this paper, we introduce the use of Convolutional Neural Network (CNN) models to predict different drugs response. DeepInsight algorithm used to convert the input data to images to be more suitable as input to the CNN. We proposed 3 different pretrained CNNs-models (InceptionV3, Xception, EfficientNetB7) with alternatives in their settings in the training process and modification in their architectures to be able to predict the drug response using IC50 regression values. Those models are selected due to their efficiency for ImageNet applications.the proposed modified Xception model achieves the best accuracy over the 2 others. At first, the whole data input passes through DeepInsight which converts the gene expression data and mutation data to images. Dimension reduction is then applied using the helper technique inside the DeepIsignt. Comparative analysis with other Deep models, shows that the proposed approach improve the prediction accuracy in a range between 14% and 22% as a reduction in mean squared error (MSE) | ||
| Keywords | ||
| Artificial Intelligence; Biomedical; Convolutional Neural Network; Drug Cancer Prediction | ||
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