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Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
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Related Experiment Video

Updated: Oct 10, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Deep-ReAP: Deep Representations And Partial label learning for Multi-pathology Classification.

Sohini Roychowdhury

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel method for training deep learning models to detect multiple pathologies in medical images, improving accuracy and explainability for complex cases.

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    Area of Science:

    • Medical image analysis
    • Artificial intelligence in diagnostics
    • Deep learning for pathology detection

    Background:

    • Automated detection of multiple pathologies in medical images is challenging due to data imbalance and patient-specific variations.
    • Existing automated systems struggle with the complexity of co-existing pathologies and diverse presentations.

    Purpose of the Study:

    • To develop a novel method for creating minimal, optimally trained datasets for deep learning models.
    • To enable explainable classification and detection of multiple, co-existing pathologies in medical images.

    Main Methods:

    • Implemented partial label learning with 1% false labels to identify under-trained pathology categories.
    • Fine-tuned deep representations to enhance classification accuracy and explainability.
    • Identified an optimal subset of 54% of training images for model training.

    Main Results:

    • Achieved explainable classification for up to 7 co-existing pathological categories in retinal images.
    • The model demonstrated high performance with overall precision/recall/Fβ scores of 57%/87%/80%.
    • Successfully handled 36 various combinations of co-existing pathologies.

    Conclusions:

    • The proposed method effectively reduces dataset size while maintaining high performance for multi-pathology detection.
    • This approach facilitates explainable inferencing for multi-label medical image datasets.
    • The method addresses key challenges in automated medical diagnostics, particularly data imbalance and variability.