Multi CNN based automatic detection of mitotic nuclei in breast histopathological images

In breast cancer diagnosis, the number of mitotic cells in a specific area is an important measure. It indicates how far the tumour has spread, which has consequences for forecasting the aggressiveness of cancer. Mitosis counting is a time-consuming and challenging technique that a pathologist does manually by examining Hematoxylin and Eosin (H&E) stained biopsy slices under a microscope. Due to limited datasets and the resemblance between mitotic and non-mitotic cells, detecting mitosis in H&E stained slices is difficult. By assisting in the screening, identifying, and labelling of mitotic cells, computer-aided mitosis detection technologies make the entire procedure much easier. For computer-aided detection approaches of smaller datasets, pre-trained convolutional neural networks are extensively employed. The usefulness of a multi CNN framework with three pre-trained CNNs is investigated in this research for mitosis detection. Features were collected from histopathology data and identified using VGG16, ResNet50, and DenseNet201 pre-trained networks. The proposed framework utilises all training folders of the MITOS dataset provided for the MITOS-ATYPIA contest 2014 and all the 73 folders of the TUPAC16 dataset. Each pre-trained Convolutional Neural Network model, such as VGG16, ResNet50 and DenseNet201, provides an accuracy of 83.22%, 73.67%, and 81.75%, respectively. Different combinations of these pre-trained CNNs constitute a multi CNN framework. Performance measures of multi CNN consisting of 3 pre-trained CNNs with Linear SVM give 93.81% precision and 92.41% F1-score compared to multi CNN combinations with other classifiers such as Adaboost and Random Forest.

Automated summary

In breast cancer diagnosis, the number of mitotic cells is important for determining the aggressiveness of cancer. Computer-aided mitosis detection technologies can assist in screening and labeling mitotic cells, making the process easier. This study explored the usefulness of a multi CNN framework with pre-trained VGG16, ResNet50, and DenseNet201 models for mitosis detection, achieving a precision of 93.81% and F1-score of 92.41%.