Lung Cancer Detection and Classification on DICOM Dataset

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Overview and Steps for Lung Cancer Detection on DICOM Dataset

To train a machine learning model that can detect lung cancer from DICOM images.

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

The procedure starts from collecting the Images on which the model should be trained, then area which should be detected is label for the training set after that the annotation of the images are required which will be followed by pre-processing them. If the number of images are lesser than the required number of images than we can increase the number by image augmentation process. Next, the dataset will be divided into training and testing. Training the model will be done. The model can be ML/DL model but according to the aim DL model will be preferred. The model will be tested in the under testing phase which will be used to detect the detect the lung cancer the uploaded images.

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

Steps for Building the Machine Learning Model based platform 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

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In Which Agentic Platform and Accelerator you are Interested? *

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At what stage is your AI use case currently in? *

What are the primary challenges in adopting AI? *

What kind of infrastructure does your organization currently using? *

Are you using any Data platform? *

Preferred Approach for AI Transformation *

In Which Domain your Solution/Organization belongs to in-terms of Data Privacy, Trustworthy AI *

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Lung Cancer Detection and Classification on DICOM Dataset

Table of Content

In this Article

Additional Resources

Overview and Steps for Lung Cancer Detection on DICOM Dataset

Steps of the Process

Solution Offered for Building the Detection Platform

Machine Learning and Deep Learning Models

Architecture for Building Machine Learning Model for Cancer Detection

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