Abstract

Tumors and cancers are the major diseases in humans and early prediction of cancers can save human lives. Cancer genome sequencing involves sequencing the whole genome of a group of cancer cells. It is a chemical laboratory approach for describing and identifying cancerous RNA or DNA cells.Therefore, this work develops adeep learning algorithm for classifying the cancer diseases from genomic sequences. Initially, Ada-boost descriptor is used to extract the deep features from the dataset, which identifies the inter and intra relationship between multiple correlated cancers. Then, deep learning based AlexNet model is used to classify the different types of cancers. Further, the simulation results shows that the proposed method resulted in superior performance as compared to state of art approaches

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