Nodule Generation of Lung CT Images using a 3D Convolutional LSTM Network

EasyChair Preprint 4316

Abstract

In the US, the American Cancer Society report for 2020 estimatesabout 228,820 new cases which could result in 135,720 deaths which translatesto 371 deaths per day compared to the overall daily cancer death of 1660. TheCancer Society of South Africa, (CANSA) reports that lung cancer and otherchronic lung diseases are leading causes of death nationally. Research in this areais necessary in order to reduce the number of reported deaths through early detection and diagnosis. A number of studies have been done using datasets forComputed Tomography (CT) images in the diagnosis and prognosis by oncologists, radiologists and medical professionals in the healthcare sector and a number of machine learning methods are being developed using conventional neuralnetworks (CNN) for feature extraction and binary classification with just a fewresearches making use of combined(hybrid) methods that have shown the capability to increase performance and accuracy in prediction and detection of earlystage onset of lung cancer. In this paper, a combined model is proposed using3D images as input to a combination of a CNN and long short-term memory(LSTM) network which is a type of recurrent neural network (RNN). The hybridization which often lead to increase need for computational resources will beadjusted by improving the nodule generation to focus only on the search spacearound the lung nodules, this proposed model requires less computation resources, avoiding the need to adding the whole 3D CT image into the network,therefore only the region of interest near candidate regions with nodules will bepre-processed. The results of previous traditional CNN architecture is comparedto this combined 3D Convolutional LSTM for nodule generation. In the experiments, the proposed hybrid model overperforms the traditional CNN architecturewhich shows how much improvement a hybridization of suitable models can contribute to lung cancer resear

Keyphrases: CNN, ConvLSTM, Ensemble network, LSTM, Lung cancer diagnosis, deep learning

@booklet{EasyChair:4316,
  author    = {Kolawole Olulana and Pius Owolawi and Chunling Tu and Bolanle Abe},
  title     = {Nodule Generation of Lung CT Images using a 3D Convolutional LSTM Network},
  howpublished = {EasyChair Preprint 4316},
  year      = {EasyChair, 2020}}