XenonStack Use Case Lung Cancer Detection and Classification on DICOM Dataset

Overview and Steps for Lung Cancer Detection on DICOM Dataset

Steps of the Process

• Collection of Images in DICOM Format
• Conversion of the images and Labeling the Images
• Annotate all the Images
• Image pre-processing
• Image Augmentation
• Dividing the train and test data set
• Training of the Model
• Testing the Model

Solution Offered for Building the Detection Platform

Machine Learning and Deep Learning Models

• Region-based Convolutional Network (R-CNN)
• Fast Region-based Convolutional Network (Fast R-CNN)
• Faster Region-based Convolutional Network (Faster R-CNN)
• Region-based Fully Convolutional Network (R-FCN)
• Neural Architecture Search Net (NASNet)
• Generative Adversarial Networks (GANs)

Architecture for Building Machine Learning Model for Cancer Detection

• The first step to be followed is import new Images
• Next step is to label the images by using Labelme tool.
• Image Pre-processing
• Image Augmentation
• Dividing the train and test dataset
• Model Building (GAN)
• Training and Evaluation of GAN model