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

TL;DR

This study systematically evaluates the diagnostic power of simple color features in histopathology images, demonstrating their effectiveness in cancer classification independent of morphological information.

Contribution

It provides the first comprehensive analysis of how global color features alone can support cancer diagnosis, highlighting their potential as efficient pre-screening tools.

Findings

Color features achieved up to 89% accuracy in binary cancer classification.

Global chromatic shifts are associated with malignancy and carry diagnostic information.

Color-based models outperform random baselines, indicating non-random, relevant signals.

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.