Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · Radiomics and Machine Learning in Medical Imaging · Smart Systems and Machine Learning
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Technology and Telecommunications Engineering, University of Granada, 18071 Granada, Spain
Department of Applied Mathematics, University of Granada, 18071 Granada, Spain
Funding
Spanish Ministry of Sciences, Innovation, and Universities · Government of Andalusia
References (80)
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