Mathematical Model for Tissue Pathologies
Year: 2025
Type: conference-paper
Abstract: To overcome the time intensive and protocol dependent nature of traditional histopathological analysis for tumor identification, a mathematical model can be developed based on the distinct density profiles of different tissue types. The foundation of this model lies in assigning quantitative density values: healthy tissue would be characterized by a relatively consistent and organ-specific density range, serving as a baseline. Lipoma, being composed of adipose tissue which is less dense than water, would be mathematically defined by a significantly lower density value. In contrast, a cancerous or malignant tumor, with its disorganized architecture, higher cellular density, and regions of necrosis and irregular angiogenesis, would be represented by a density that is both different and typically more heterogeneous than healthy tissue or lipomas. By analyzing tissue samples through imaging or other modalities that infer density, this model could rapidly classify tissue as healthy, lipomatous, or potentially malignant, providing a crucial and expedited initial screening tool to guide further diagnostic efforts.
Author: Umair Masood Awan
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7105542637
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