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An Analysis of Deep Learning Architectures for Cancer Diagnosis

Abstract:

It was analyzed the reference information on Deep Learning applications in the areas of diagnosis and pbkp_rediction of different types of cancer. The problem is to perform the analysis and obtain the criteria to select a Deep Learning architecture for cancer diagnosis. The objective is to carry out an analysis of Deep Learning architectures and select a model to apply training and tests that assist in the diagnosis of cancer. It was used as a method the exploratory research and deduction to analyze the reference information on Deep Learning theories and architectures applied in cancer diagnosis; it also describes the reasons for selecting a model, scope, proposal, configuration parameters and structure for a CNN network. It resulted in the Impact of Deep Learning in cancer diagnosis, Training the CNN network, and Testing the CNN network. It was concluded that the 9-layer CNN Simple model used for training and testing on a data set of 8801 breast cancer images, has good properties and generates quantitative results for image classification; in the adopted model, a precision rate was obtained on the data set that reached 85.67% in training and 85.87% in tests; the quality of the model in classification tasks is 86%; this indicates the good stability and efficiency of the model.