Tabatabaei, ZahraPérez Bueno, FernandoColomer, AdriánOliver Moll, JavierMolina, RafaelNaranjo Ornedo, Valeriana2024-04-182024-04-182024-03https://riunet.upv.es/handle/10251/203601[EN] Content-Based Histopathological Image Retrieval (CBHIR) is a search technique based on the visual content and histopathological features of whole-slide images (WSIs). CBHIR tools assist pathologists to obtain a faster and more accurate cancer diagnosis. Stain variation between hospitals hampers the performance of CBHIR tools. This paper explores the effects of color normalization (CN) in a recently proposed CBHIR approach to tackle this issue. In this paper, three different CN techniques were used on the CAMELYON17 (CAM17) data set, which is a breast cancer data set. CAM17 consists of images taken using different staining protocols and scanners in five hospitals. Our experiments reveal that a proper CN technique, which can transfer the color version into the most similar median values, has a positive impact on the retrieval performance of the proposed CBHIR framework. According to the obtained results, using CN as a pre-processing step can improve the accuracy of the proposed CBHIR framework to 97% (a 14% increase), compared to working with the original images.Reconocimiento (by)ESTADISTICA E INVESTIGACION OPERATIVATEORÍA DE LA SEÑAL Y COMUNICACIONESArtículo10.3390/app14052063Abierto2076-3417