Fuzzy ensemble of deep learning models using choquet fuzzy integral, coalition game and information theory for breast cancer histology classification

Abstract Millions of women, worldwide, suffer from breast cancer and a large number of them succumb to death. In recent years, computer-aided diagnosis (CAD) systems are being developed for the detection of Breast Cancer. A number of fusion techniques have been proposed in this domain, but none of them take into consideration the decisions taken by a subset of classifiers during fusion. Our method, which uses Choquet Integral, considers subsets of classifiers and is thus stronger than the existing methods and beat all of these existing fusion methods in terms of accuracy. This however poses a significant challenge in terms of complexity, since the calculation of the fuzzy measures is a complicated and complex task, which we have dealt with using a novel heuristic method by employing Coalition Game, Information Theory, and by defining a novel mathematical function. In the present work, we have fused VGG16, VGG19, Xception, Inception V3, and InceptionResnet V2 for the classification of breast cancer histology images using a Choquet integral, Coalition game theory, and Information theory. The dataset used for evaluating the proposed model is the ICIAR 2018 Grand Challenge on Breast Cancer Histology (popularly known as BACH) images, which consist of 2-class and 4-class problems. To the best of our knowledge, our experimental results outperform almost all the state-of-the-art methods. For the two-class problem, the best test accuracy among the five deep learning models was achieved by Xception and it was 95% while the Fusion method has a test accuracy of 96%. For the four-class problem, Xception and InceptionResnet V2 have achieved the best test accuracy and both have a test accuracy of 91% while the Fusion method has a test accuracy of 95%. Again, in the case of the two-class problem the best precision and recall by the deep learning models are 0.95 and 0.95 respectively, while the precision and recall for after fusion are 0.96 and 0.96 respectively which is an increase of .01. In the case of the four-class problem, the best precision and recall by the deep learning models are 0.91 and 0.91 respectively, while the precision and recall after fusion are 0.95 and 0.95 respectively which is a very significant increase of .04. The source code for this project can be accessed at https://github.com/subhankar01/fuzzyBACH