More than 1.7 million women worldwide and 300,000 thousand women in the US are affected by breast cancer each year. The cancer classification model can help healthcare organizations especially in developing countries to improve their services and reduce their costs for the patients.

What it does

The classification of malignant and Benign breast cancers from the features that represent characteristics of cell nuclei of images generated after the Fine Needle Aspiration Method.

How I built it

The cancer classification project is using a scikit-learn model to classify the malignant and breast cancers and the model has been onboarded and published on the Acumos marketpace. We are using the Breast Cancer Wisconsin Diagnostic dataset  from the UCI Machine Learning Repository to predict the Malignant or Benign cancer. RandomForest classification is used on these thirty features to predict the malignant or benign cancers over the Acumos Python Client Repository. The test data is available in the Documents on the Acumos marketplace and anyone can communicate with the microservice and test their own similar data or the data given to classify the malignant and benign breast cancers.

Challenges I ran into

The large number of feature variables in the dataset and learning about the Acumos Python Client Repository

Accomplishments that I'm proud of

Learning the Acumos platform and creating a model that can benefit and bring about a positive change in the world for many people around the world on this open-source platform

What I learned

Using the Acumos platform and marketplace and open-source future of AI on the Acumos platform

What's next for Cancer Classification

The future scope of this cancer classification Acumos project would be the improvement of the model performance and applications that would benefit healthcare professionals. Classification on digitized images of fine needle aspirate (FNA) data using convolutional neural networks. Similarly, the Acumos platform can be used for models to diagnose or classify diseases from X-rays, CT scan and MRI images

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