The main purpose of this research was to introduce a classification method, which combines a rule induction procedure with the Takagi–Sugeno inference model. This proposal is a continuation of our previous research, in which a classification process based on interval type-2 fuzzy rule induction was introduced. The research goal was to verify if the Mamdani fuzzy inference used in our previous research could be replaced with the first-order Takagi–Sugeno inference system. In the both cases to induce fuzzy rules, a new concept of a fuzzy information system was defined in order to deal with interval type-2 fuzzy sets. Additionally, the introduced rule induction assumes an optimization procedure concerning the footprint of uncertainty of the considered type-2 fuzzy sets. A key point in the concept proposed is the generalization of the fuzzy information systems’ attribute information to handle uncertainty, which occurs in real data. For experimental purposes, the classification method was tested on different classification benchmark data and very promising results were achieved. For the data sets: Breast Cancer Data, Breast Cancer Wisconsin, Data Banknote Authentication, HTRU 2 and Ionosphere, the following F-scores were achieved, respectively: 97.6%, 96%, 100%, 87.8%, and 89.4%. The results proved the possibility of applying the Takagi–Sugeno model in the classification concept. The model parameters were optimized using an evolutionary strategy.

In this study, we introduce a data augmentation procedure for histopathology image classification. This is an extension to our previous research, in which we showed the possibility to apply deep learning for morphological analysis of tumour cells. The research problem considered, aimed to distinguish how many cells are located in a structure composed of overlapping cells. We proved that the calculation of the tumour cell number is possible with convolutional neural networks. In this research, we examined the possibility to generate synthetic training data set and to use it for the same purpose. The lack of large data sets is a critical problem in medical image classification and classical augmentation procedures are not sufficient. Therefore, we introduce completely new augmentation approach for histopathology images and we prove the possibility to apply it for a cell-counting problem.

In this study, we introduce a morphological analysis of segmented tumour cells from histopathology images concerning the recognition of cell overlapping. The main research problem considered is to distinguish how many cells are located in a structure, which is composed of overlapping cells. In our experiments, we used convolutional neural network models to provide recognition of the number of cells. For the medical data used: Ki-67 histopathology images, we achieved a high f1-score result. Therefore, our research proves the assumption to use convolutional neural networks for morphological analysis of segmented objects derived from medical images.

In this research, we introduce a classification procedure based on rule induction and fuzzy reasoning. The classifier generalizes attribute information to handle uncertainty, which often occurs in real data. To induce fuzzy rules, we define the corresponding fuzzy information system. A transformation of the derived rules into interval type-2 fuzzy rules is provided as well. The fuzzification applied is optimized with respect to the footprint of uncertainty of the corresponding type-2 fuzzy sets. The classification process is related to a Mamdani type fuzzy inference. The method proposed was evaluated by the F-score measure on benchmark data.