Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images
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Deep learningCancerImage preprocessing methodANOVA
Murcia-Gómez, D.; Rojas-Valenzuela, I.; Valenzuela, O. Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images. Appl. Sci. 2022, 12, 11375. [https://doi.org/10.3390/app122211375]
PatrocinadorSpanish Government PID2021-128317OB-I00; Government of Andalusia P20-00163
Early diagnosis of cancer is very important as it significantly increases the chances of appropriate treatment and survival. To this end, Deep Learning models are increasingly used in the classification and segmentation of histopathological images, as they obtain high accuracy index and can help specialists. In most cases, images need to be preprocessed for these models to work correctly. In this paper, a comparative study of different preprocessing methods and deep learning models for a set of breast cancer images is presented. For this purpose, the statistical test ANOVA with data obtained from the performance of five different deep learning models is analyzed. An important conclusion from this test can be obtained; from the point of view of the accuracy of the system, the main repercussion is the deep learning models used, however, the filter used for the preprocessing of the image, has no statistical significance for the behavior of the system.