Laura GongasAna M. MorenoLaura Maria Bravo2026-03-222026-03-22201810.1109/sib.2018.8467730https://doi.org/10.1109/sib.2018.8467730https://andeanlibrary.org/handle/123456789/54394Citaciones: 4This paper discusses automated breast cancer diagnosis based on histological images. The dataset consists of four different groups: normal tissue, benign carcinoma, in situ carcinoma and invasive carcinoma. We developed two algorithms to classify the images into these categories. Both include a preprocessing stage for noise elimination and cell segmentation, extraction of features and final diagnosis of the tissue along with malignity degree. The diagnosis is executed by classification using k-means, random forests and support vector machines. The best experiment resulted in an ACA of 0.475.enBreast cancerComputer scienceArtificial intelligenceComputer visionCancerMedicinearticle