… This section presents recent achievements in lung cancer and nodule prediction using
deep learning techniques. The processing includes image pre-processing, lung nodule …

… of using deep learning algorithms for lung cancer diagnosis with the cases from Lung Image
… Three deep learning algorithms were designed and implemented, including Convolutional …

… cancerous lung nodules from the given input lung image and to classify the lung cancer and
… To detect the location of the cancerous lung nodules, this work uses novel Deep learning …

… participants with only benign lung nodules and 1058 participants with lung cancer. In the …
lung cancer (N = 932 in 575 patients). We included all nodules in patients without a lung cancer …

… : First phase is the CT lung cancer classification processes where the selected features …
deep learning classifier with MGSA optimization algorithm is used to classify the CT lung cancer …

… deep learning algorithm that uses a patient’s current and prior computed tomography volumes
to predict the risk of lung cancer… ) on 6,716 National Lung Cancer Screening Trial cases, …

… , we evaluated deep learning networks for predicting clinical outcomes through analyzing
time series CT images of patients with locally advanced non–small cell lung cancer (NSCLC). …

… Here, we demonstrate how the field can further benefit from deep learning by presenting a
strategy based on convolutional neural networks (CNNs) that not only outperforms methods in …

… The goal of this work was to non-invasively predict lung cancer histology and develop robust
deep-learning based radiomics models to help differentiate clinically important histologic …

… -stage lung cancer and to help physicians and researchers in this field. The main purpose
of this work is to identify the challenges that exist in lung cancer based on deep learning. The …