New fully automatic approach for tissue identification in histopathological examinations using transfer learning

The use of computational techniques in the processing of histopathological images allows the study of the structural organization of tissues and their changes through diseases. This study aims to develop a tool for classifying histopathological images from breast lesions in the benign and malignant classes through magnification scales by an innovative way of using transfer learning techniques combined with machine learning methods and deep learning. The BreakHis dataset was used in the experiments, consisting of histopathological images of breast cancer with different tumor enlargement scales classified as Malignant or Benign. In this study, various combinations of Extractor-Classifiers were performed, thus seeking to compare the best model. Among the results achieved, the best Extractor-Classifier set formed was CNN DenseNet201, acting as an extractor, with the SVM RBF classifier, obtaining accuracy of 95.39% and precision of 95.43% for the 200X magnification factor. Different models were generated, compared to each other, and validated based on methods in the literature to validate the experiments, thus showing the effectiveness of the proposed model. The proposed method obtained satisfactory results, reaching results in the state-of-the-art for the multi-classification of subclasses from the different scale factors found in the BreakHis dataset and obtaining better results in the classification time.