Deep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing

Author(s): Sushila Ratre*, Nehha Seetharaman and Aqib Ali Sayed

DOI: 10.2174/9789815079210123010007

Deep Learning For Lung Cancer Detection

Pp: 47-59 (13)

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Abstract

SHS investigation development is considered from the geographical and historical viewpoint. 3 stages are described. Within Stage 1 the work was carried out in the Department of the Institute of Chemical Physics in Chernogolovka where the scientific discovery had been made. At Stage 2 the interest to SHS arose in different cities and towns of the former USSR. Within Stage 3 SHS entered the international scene. Now SHS processes and products are being studied in more than 50 countries.

Abstract

By detecting lung cancer in advance, doctors can make the right decision to treat patients to ensure that they live long and healthy lives. This research aims to build a CNN model using a pre-trained model and functional API that would classify if a person had lung cancer or not based on a CT scan. This research uses CT scan images as input for the prediction model from the LUNA16 [Luna Nodule Analysis 2016] dataset for experimenting by using ResNet 50 and VGG 16. ResNet50 showed slightly high accuracy on test data compared to VGG16, which is 98%.

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