Deep Learning For Healthcare

Foundational Deep Learning Architectures in Healthcare

1. CNN (Convolutional Neural Networks)

Core Principles:

CNNs are based on hierarchical feature extraction and spatial pattern recognition. These models excel at capturing visual features through layers of convolution and pooling operations.

Primary Healthcare Applications:

CNNs are widely applied in medical imaging tasks, including:

• Classification
• Segmentation
• Detection
• Disease prediction, such as:
  • Diabetic retinopathy
  • Alzheimer’s disease
  • Various types of cancer

Key SOTA Examples/Papers:

• ResNet
• VGGNet
• EfficientNet
• LaNet (Biswas et al., 2019)

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2. RNN (Recurrent Neural Networks) and Variants (LSTMs, GRUs)

Core Principles:

RNNs process sequential data by capturing temporal dependencies and maintaining a form of memory across time steps, which makes them ideal for time-series data.

Primary Healthcare Applications:

• Electronic Health Record (EHR) Analysis:
  • Temporal event extraction
  • Outcome prediction
• Wearable Data Analysis:
  • Physiological signal monitoring
  • Activity recognition

Key SOTA Examples/Papers:

• LSTM
• GRU (Shickel et al., 2018)

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3. Transformer-Based Models

Core Principles:

Transformers leverage self-attention mechanisms to capture long-range dependencies and provide global context understanding, which enhances performance on complex datasets.

Primary Healthcare Applications:

• Medical Imaging:
  • Complex analysis
  • Abnormality detection
  • 3D segmentation
  • Denoising
• Predictive Analytics
• Drug Development

Key SOTA Examples/Papers:

• UNETR++
• Spach Transformer
• Vision Transformer (ViT) (Kothinti, 2025)

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4. Generative AI (GANs, Diffusion Models)

Core Principles:

Generative AI models are designed for data synthesis and generation. They can learn complex data distributions and recreate realistic, high-fidelity samples.

Primary Healthcare Applications:

• Synthetic Data Generation:
  Used for privacy preservation and data security.
• Drug Discovery:
  • De novo molecular design
  • Molecular enhancement
• Personalized Medicine

Key SOTA Examples/Papers:

• GANs
• Denoising Diffusion Probabilistic Models
• GenMol (Kothinti, 2025)

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5. Emerging Architectures (e.g., KANs)

Core Principles:

Emerging models like KANs (Kernel-based Artificial Neurons) offer learnable function approximation, greater nonlinearity, and potentially better interpretability.

Primary Healthcare Applications:

• Medical image classification, particularly in capturing complex pathological structures.

Key SOTA Examples/Papers:

• MedKAN (Yang et al., 2025)

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DL Branches in Healthcare

• Medical Image Analysis
• Electronic Health Record Analysis
• Wearable Device Data Analysis
• Drug Discovery and Development
• Genomics and Personalized Medicine

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References

Biswas, M., Kuppili, V., Saba, L., Edla, D. R., Suri, H. S., Cuadrado-Godia, E., Laird, J. R., Marinhoe, R. T., Sanches, J. M., Nicolaides, A., & Suri, J. S. (2019). State-of-the-art review on deep learning in medical imaging. FBL, 24(3), 380–406. https://doi.org/10.2741/4725

Kothinti, R. R. (2025). Deep learning in healthcare: Transforming disease diagnosis, personalized treatment, and clinical decision-making through AI-driven innovations. World Journal of Advanced Research and Reviews, 2841–2856. https://doi.org/10.30574/wjarr.2024.24.2.3435

Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018). Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. IEEE Journal of Biomedical and Health Informatics, 22(5), 1589–1604. https://doi.org/10.1109/JBHI.2017.2767063

Yang, Z., Zhang, J., Luo, X., Lu, Z., & Shen, L. (2025). MedKAN: An Advanced Kolmogorov-Arnold Network for Medical Image Classification. arXiv Preprint arXiv:2502.18416. https://doi.org/10.48550/arXiv.2502.18416