Artificial Neural Networks in Non-Small Cell Lung Cancer Detection using Computed Tomography Medical Imaging

On average, lung cancer is the cancer that kills the most people in the world due to its complex detection and cancer variants such as small and non-small cell cancers, with non-small cell cancers being the most common. The objective of this project is to compare different architectures of convolutional neural networks (CNN) such as: ResNet50, RESNET101, DenseNet201, EfficientNetB4, VGG16 and VGG19, which have been trained with the same database of approximately 1500 computer tomography images (CT) for the detection of the type of non-small cell lung cancer such as: squamous cell, adenocarcinoma and large cell. It was found among all the neural networks that the most reliable for time, space and accuracy is ResNet50, so it seeks to compare the accuracy of the ResNet50 neural network with other investigations, in order to compare the different architectures to see which one has a better accuracy in the detection of lung cancer.