Beyond Morphology: Quantifying the Diagnostic Power of Color Features in Cancer Classification

2026-05-18

AI Summary

This study investigates whether color features alone—without reliance on morphological information—can effectively support binary cancer diagnosis. By extracting statistical color moments and discretized RGB/HSV histograms, and integrating them with classical machine learning classifiers, the discriminative capacity of global color features is systematically evaluated across ten experimental configurations. The results demonstrate that the raw color distribution of images alone captures non-random diagnostic signals associated with malignancy, achieving a classification accuracy of 89%, which significantly outperforms random baselines. This work provides the first clear validation that color features inherently possess reliable diagnostic potential, offering both theoretical grounding and a practical pathway for developing lightweight models for initial cancer screening.

Abstract

In histopathology, human experts primarily rely on color as a means of enhancing contrast to interpret tissue morphology, whereas machine vision models process color as raw statistical information. This distinction raises a fundamental question: to what extent can pixel intensity alone, independent of structural and morphological cues, support cancer classification? To address this question, we systematically evaluated the standalone discriminative power of global color features while deliberately excluding all morphological information. Specifically, we extracted statistical color moments and discretized RGB and HSV color histograms, and assessed their performance across ten diverse experimental settings using classical machine learning classifiers. Our results demonstrate that color features alone can achieve strong performance in binary diagnostic tasks (e.g., benign versus malignant), with classification accuracies reaching up to 89%. This performance is likely attributable to global chromatic shifts associated with malignancy. Importantly, these simple color-based representations consistently outperformed random baselines by a substantial margin, indicating that raw color distributions encode a non-random and diagnostically relevant signal for cancer detection. Consequently, this study suggests that simple, computationally efficient color features can serve as an effective pre-screening tool. By identifying samples with strong chromatic indicators of malignancy, these lightweight models could function as a first-pass triage system, reducing the computational burden on complex deep learning architectures.

Problem

• color features
• cancer classification
• histopathology
• morphology-independent
• diagnostic power

Innovation

• color features
• cancer classification
• histopathology
• morphology-independent
• diagnostic triage