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A Comparative Analysis of Automated ML (AutoML) vs. Custom Deep Learning Models for Medical Image Classification on Azure

AutoML

AutoML vs Custom Deep Learning Models for Medical Image Classification on Azure

  • Use Case:

    Healthcare institutions often need fast, accurate, and scalable medical image classification systems to assist in diagnosing conditions like tumors, pneumonia, or diabetic retinopathy.

    AutoML on Azure offers a low-code/no-code way to generate optimized models quickly.

    Custom Deep Learning Models provide flexibility, higher accuracy, and domain-specific fine-tuning.

    This project helps hospitals and research labs decide whether to adopt Azure AutoML Vision or build custom CNN/transfer learning models on Azure.

Objective

  • To compare AutoML and custom deep learning approaches in terms of:

    Accuracy (Precision, Recall, F1, AUC)

    Training time & inference time

    Cost & scalability on Azure

    Ease of deployment and maintainability

    To recommend the best approach for real-world medical imaging applications on Azure.

Project Description

  • Data Preparation:

    Use publicly available datasets like ChestX-ray14, ISIC (skin cancer), or COVIDx.

    Upload to Azure Blob Storage or Azure Data Lake.

    Perform preprocessing (resizing, augmentation, normalization).

Model Development

  • Approach 1 : AutoML (Azure AutoML Vision)

    Upload dataset to Azure Machine Learning Studio.

    Let AutoML automatically train multiple classification models.

    Evaluate best-performing models.
  • Approach 2: Custom Deep Learning

    Build CNNs and Transfer Learning models (ResNet, DenseNet, EfficientNet) using PyTorch/TensorFlow.

    Train models on Azure Machine Learning Compute (GPU VMs).

    Use HyperDrive (Azure hyperparameter tuning) for optimization.

Evaluation & Benchmarking

  • Compare AutoML models vs. custom models using:

    Accuracy, Precision, Recall, F1-Score, ROC-AUC

    Training cost (GPU hours)

    Inference latency

    Deployment complexity

Deployment

  • Deploy both models as REST endpoints using Azure Kubernetes Service (AKS) or Azure Container Instances (ACI).
  • Expose APIs for integration into healthcare applications.

Comparison Insights

  • AutoML = faster development, easier deployment, but less customization.
  • Custom Models = more accurate for complex cases, but require expertise & resources.
  • Key Technologies & Azure Services :
    Azure Service Purpose
    Azure Machine Learning Service Centralized platform for training, AutoML, experiment management, and tracking workflows
    Azure AutoML Vision Automated ML for image classification with minimal coding effort
    Azure Data Lake / Azure Blob Storage Storage of large-scale medical imaging datasets (X-rays, MRIs, CT scans)
    Azure Compute Instances / GPU VMs High-performance compute for training custom deep learning models
    Azure ML HyperDrive Hyperparameter tuning to optimize deep learning models
    Azure Kubernetes Service (AKS) / Azure Container Instances (ACI) Deployment of trained models as scalable APIs or microservices
    Azure Monitor & Application Insights Continuous monitoring, logging, and performance tracking of deployed models