Comparative Analysis of CNN Regularisation and Augmentation Techniques with Ten Layer Deep Learning Model To Detect Lung Cancer
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Abstract
In the medical sector cancer detection became the most challenging task. Here a lot of research is carried out by the scientific fraternity. Most medical issues are getting better answers because to modern technology like artificial intelligence and models based on neural networks. In this the first half part of the paper discuss about the CNN model by using regularization and augmentation techniques for getting the better accuracy result. The second part delas with developing and demonstrating an application for detecting the lung cancer using the deep learning (DL). Here the application is built using flask which works based on the Python programming language. This acts as an application programming interface (API) between the cloud server and the proposed application model. Heroku cloud platform was used as a service base to launch the software and to use the application with highest reliability. The internal functionality of the proposed model is based on convolutional neural network (CNN) architecture with ten layers to obtain high accuracy. The model demonstrated a considerable training and validation accuracy of 94% and 92% respectively.
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References
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