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- W3111878992 abstract "In the US, the American Cancer Society report for 2020 estimates about 228,820 new cases which could result in 135,720 deaths which translates to 371 deaths per day compared to the overall daily cancer death of 1660. The Cancer Society of South Africa (CANSA) reports that lung cancer and other chronic lung diseases are leading causes of death nationally. Research in this area is necessary in order to reduce the number of reported deaths through early detection and diagnosis. A number of studies have been done using datasets for Computed 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 neural networks (CNN) for feature extraction and binary classification with just a few researches making use of combined (hybrid) methods that have shown the capability to increase performance and accuracy in prediction and detection of early stage onset of lung cancer. In this paper, a combined model is proposed using 3D 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 be adjusted by improving the nodule generation to focus only on the search space around 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 be pre-processed. The results of previous traditional CNN architecture is compared to this combined 3D Convolutional LSTM for nodule generation. In the experiments, the proposed hybrid model overperforms the traditional CNN architecture which shows how much improvement a hybridization of suitable models can contribute to lung cancer research." @default.
- W3111878992 created "2020-12-21" @default.
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- W3111878992 date "2020-01-01" @default.
- W3111878992 modified "2023-10-06" @default.
- W3111878992 title "Nodule Generation of Lung CT Images Using a 3D Convolutional LSTM Network" @default.
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- W3111878992 doi "https://doi.org/10.1007/978-3-030-64559-5_60" @default.
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