Retracted: Hybrid Deep Learning Algorithm for Pulmonary Nodule Detection using Body Sensors

The leading cause of death in the world is lung cancer. Routine screening is important because it improves outcomes, especially for some risk populations. In addition to low-dose computed tomography, chest X-rays can be used as a screening tool. Software diagnosis systems usually on the basis of convolutional networks (CNNs) have demonstrated their capacity to recognize nodules on radiographs and may therefore help physicians in clinical practice. Lung cancer, also referred to as malignant tumors, is an infectious lung tumour brought on by unchecked granulation tissue in the lungs. This article describes a method for applying Deep Learning to identify cancerous pulmonary nodules in CT scans. The sections of the lungs on the scans were filtered out using a preprocessing procedure. A three dimensional CNN model built on the C3D network architecture was then used to extract the attributes. For the purpose of reducing false-positives, the LIDC-IDRI is the main dataset utilized in addition to a few tools from the LUNA16 grand challenge. The final result is a framework that delineates the relevant regions from the CT scans and forecasts the positions of malignant lung nodules. The final algorithm was able to identify malignant pulmonary nodules and determine their malignancy ratings with an 96% specificity.