Automated carcinoma classification using efficient nuclei-based patch selection and deep learning techniques

Abstract

Breast cancer can be successfully treated if diagnosed at its earliest, though it is considered as a fatal disease among women. The histopathology slide turned images are the gold standard for tumor diagnosis. However, the manual diagnosis is still tedious due to its structural complexity. With the advent of computer-aided diagnosis, time and computation intensive manual procedure can be managed with the development of an automated classification system. The feature extraction and classification are quite challenging as these images involve complex structures and overlapping nuclei. A novel nuclei-based patch extraction method is proposed for the extraction of non-overlapping nuclei patches obtained from the breast tumor dataset. An ensemble of pre-trained models is used to extract the discriminating features from the identified and augmented non-overlapping nuclei patches. The discriminative features are further fused using p-norm pooling technique and are classified using a LightGBM classifier with 10-fold cross-validation. The obtained results showed an increase in the overall performance in terms of accuracy, sensitivity, specificity, and precision. The proposed framework yielded an accuracy of 98.3% for binary class classification and 95.1% for multi-class classification on ICIAR 2018 dataset.