Endometrioid Ovarian Carcinoma Classification through Color Scheme Transformation and Radiomics in Histopathological Images

• Breno A. Tamanini IFMA
• Victor G. O. Sousa IFMA
• Lissandro P. S. C. Rodrigues IFMA
• Danilo M. Oliveira IFMA
• Calebe X. Dias IFMA
• Luana B. da Cruz UFCA
• João O. B. Diniz IFMA
• Luiz O. O. Souza Júnior IFMA

DOI: https://doi.org/10.5753/sbcas.2025.6930

Abstract

Endometrioid ovarian carcinoma (EOC) is a subtype of ovarian cancer with a challenging diagnosis in histopathological images. Convolutional Neural Networks (CNNs) are widely used for this task but require large amounts of data and high computational burden. This work proposes an approach based on color scheme transformation, Radiomics, and incremental feature selection (I-RFE), applied to multiple classifiers. The experiments showed that the proposed method outperforms CNNs in the literature, achieving an accuracy of 97.96% and an F1-Score of 98.26%, while the best CNN reached 75% accuracy. These findings suggest that the approach can improve EOC detection, making it a promising alternative for diagnostic support.

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