Authors:
Gleidson Barbosa
1
;
Larissa Moreira
1
;
2
;
Pedro Moises de Sousa
1
;
Rodrigo Moreira
1
and
André Backes
3
Affiliations:
1
Institute of Exacts and Technological Sciences, Federal University of Viçosa, Rio Paranaíba-MG, Brazil
;
2
Faculty of Computing (FACOM), Federal University of Uberlândia, Uberlândia-MG, Brazil
;
3
Department of Computing, Federal University of São Carlos, São Carlos-SP, Brazil
Keyword(s):
Breast Cancer, CNN, Explainable, Influence of Factors, Classification.
Abstract:
Breast cancer is a prevalent and challenging pathology, with significant mortality rates, affecting both women and men. Despite advancements in technology, such as Computer-Aided Diagnosis (CAD) and awareness campaigns, timely and accurate diagnosis remains a crucial issue. This study investigates the performance of Convolutional Neural Networks (CNNs) in predicting and supporting breast cancer diagnosis, considering BreakHis and Biglycan datasets. Through a factorial partial method, we measured the impact of optimization and learning rate factors on the prediction model accuracy. By measuring each factor’s level of influence on the validation accuracy response variable, this paper brings valuable insights into the relevance analyses and CNN behavior. Furthermore, the study sheds light on the explainability of Artificial Intelligence (AI) through factorial partial performance evaluation design. Among the results, we determine which and how much the hyperparameters tunning influenced
the performance of the models. The findings contribute to image-based medical diagnosis field, fostering the integration of computational and machine learning approaches to enhance breast cancer diagnosis and treatment.
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