Medical image classification using a quantified hazard ratio and a multilayer fuzzy approach

Kishore Kumar Akula; Monica Akula; Alexander Gegov

doi:10.59400/cai.v2i1.450

Medical image classification using a quantified hazard ratio and a multilayer fuzzy approach

Kishore Kumar Akula Statistics eTeachers Group, Royal Statistical Society
Monica Akula Department of Neuroscience, McMaster University
Alexander Gegov School of Computing, University of Portsmouth; English Faculty of Engineering, Technical University of Sofia

Article ID: 450

2702 Views, 581 PDF Downloads

DOI: https://doi.org/10.59400/cai.v2i1.450

Keywords: cox proportional hazards model, hazard ratio, CT scans, fuzzy system, survival analysis

Abstract

We previously developed two AI-based medical automatic image classification tools using a multi-layer fuzzy approach (MFA and MCM) to convert image-based abnormality into a quantity. However, there is currently limited research on using diagnostic image assessment tools to statistically predict the hazard due to the disease. The present study introduces a novel approach that addresses a substantial research gap in the identification of hazard or risk associated with a disease using an automatically quantified image-based abnormality. The method employed to ascertain hazard in an image-based quantified abnormality was the cox proportional hazard (PH) model, a unique tool in medical research for identifying hazard related to covariates. MFA was first used to quantify the abnormality in CT scan images, and hazard plots were utilized to visually represent the hazard risk over time. Hazards corresponding to image-based abnormality were then computed for the variables, ‘gender,’ ‘age,’ and ‘smoking-status’. This integrated framework potentially minimizes false negatives, identifies patients with the highest mortality risk and facilitates timely initiation of treatment. By utilizing pre-existing patient images, this method could reduce the considerable costs associated with public health research and clinical trials. Furthermore, understanding the hazard posed by widespread global diseases like COVID-19 aids medical researchers in prompt decision-making regarding treatment and preventive measures.

References

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Published

2024-02-18

How to Cite

Akula, K. K., Akula, M., & Gegov, A. (2024). Medical image classification using a quantified hazard ratio and a multilayer fuzzy approach. Computing and Artificial Intelligence, 2(1). https://doi.org/10.59400/cai.v2i1.450

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