Sold by: MphasisÂ
Deployed on AWS
This solution provides a hybrid classical-quantum trainable ML pipeline for brain tumor classification using brain MRI images.
Overview
Brain Tumor Classification solution helps to detect type of brain tumor (glioma, meningioma, and pituitary) in brain MRI images. This solution provides pretrained hybrid classical-quantum model that is built on open-source brain MRI data of 7022 images. The solution has trainable pipeline that allows user to bring data to finetune the model to achieve required performance. Usage of quantum circuit along with Neural network architecture helps to achieve robust model performance and boost tumor detection with less tagged data.
Highlights
- The solution utilizes hybrid classical-quantum architecture to build the model which provides an advantage in the learning process. This advantage arises from the quantum representation of data, and efficient feature extraction. By harnessing quantum capabilities in combination with classical techniques, this hybrid model achieves higher accuracy and robustness on user provided data.
- The solution used open-source MRI data from Kaggle to build the pretrained model. The dataset consists of images of three types of tumors (glioma, meningioma, and pituitary) and some no tumor images. The pretraining process reduces requirement of large amounts of user data to build the model. The solution also supports user specified tumor classification schema. The solution triggers finetuning process using user provided images of tumors along with training parameters and configurations as described in usage Information.
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Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $15.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $10.00 |
ml.m5.xlarge Training Recommended | Algorithm training on the ml.m5.xlarge instance type | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $15.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $15.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $15.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $15.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $15.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $15.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $15.00 |
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Delivery details
Amazon SageMaker algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is the first version.
Additional details
Inputs
- Summary
The inference pipeline requires a zip file containing a folder data_inference which further contains images to be predicted.
- Input MIME type
- application/zip, application/gzip
https://github.com/Mphasis-ML-Marketplace/Brain-Tumor-Classification-Quantum-ML/tree/main/inference_input
https://github.com/Mphasis-ML-Marketplace/Brain-Tumor-Classification-Quantum-ML/tree/main/inference_input
Resources
Vendor resources
Support
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