Paper Contents
Abstract
Cancer is a leading cause of death worldwide, and the growing demand for early and accurate diagnosis has intensified research in automated histopathological image analysis. Histopathology, the microscopic examination of tissue samples, remains the gold standard in cancer detection. However, manual inspection is time-consuming, subjective, and prone to inter-observer variability. To address these challenges, this study focuses on exploratory data analysis (EDA) of the publicly available Histopathologic Cancer Detection dataset from Kaggle, which contains over 220,000 labeled tissue image patches. The EDA covers class distribution, pixel intensity histograms, box plots, and correlation analysis. The findings highlight balanced class representation, staining heterogeneity, and distinct intensity patterns that carry discriminative features. Furthermore, EDA reveals subtle inter-channel correlations and staining variability that are essential to understand tissue morphology. These insights not only characterize dataset quality but also provide a foundation for selecting effective preprocessing and augmentation strategies in downstream studies.
Copyright
Copyright © 2025 ANANDHI.K. This is an open access article distributed under the Creative Commons Attribution License.
