mlsanityData to 14 Oct 2026

IntelliCardiac: An Intelligent Platform for Cardiac Image Segmentation and Classification

arXiv 2505.03838 ↗First posted 5 May 2025eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

Precise and effective processing of cardiac imaging data is critical for the identification and management of the cardiovascular diseases. We introduce IntelliCardiac, a comprehensive, web-based medical image processing platform for the automatic segmentation of 4D cardiac images and disease classification, utilizing an AI model trained on the publicly accessible ACDC dataset. The system, intended for patients, cardiologists, and healthcare professionals, offers an intuitive interface and uses deep learning models to identify essential heart structures and categorize cardiac diseases. The system supports analysis of both the right and left ventricles as well as myocardium, and then classifies patient's cardiac images into five diagnostic categories: dilated cardiomyopathy, myocardial infarction, hypertrophic cardiomyopathy, right ventricular abnormality, and no disease. IntelliCardiac combines a deep learning-based segmentation model with a two-step classification pipeline. The segmentation module gains an overall accuracy of 92.6%. The classification module, trained on characteristics taken from segmented heart structures, achieves 98% accuracy in five categories. These results exceed the performance of the existing state-of-the-art methods that integrate both segmentation and classification models. IntelliCardiac, which supports real-time visualization, workflow integration, and AI-assisted diagnostics, has great potential as a scalable, accurate tool for clinical decision assistance in cardiac imaging and diagnosis.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 29 references that are in the map. First posted on arXiv 5 May 2025.

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10 of 29 references.

References · 10

  1. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  2. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  3. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  4. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 620
  5. 2021nnFormer: Interleaved Transformer for Volumetric Segmentationcited by 180
  6. 2024SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentationcited by 31
  7. 2018Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flowcited by 16
  8. 2017Automatic Segmentation and Disease Classification Using Cardiac Cine MR Imagescited by 13
  9. 2024Spatiotemporal Representation Learning for Short and Long Medical Image Time Seriescited by 2
  10. 2024S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imagingcited by 2

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