Automatic classification of tissue malignancy for breast carcinoma diagnosis

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Fondón, I (Author) Other; Sarmiento, A (Author) Other; García, AI (Author) Other; Silvestre, M (Author) Other; Eloy, C (Author) Other; Polónia, A (Author) Other; Paulo Aguiar (Author) FMUP

Journal

Title: Computers in Biology and Medicine

Vol. 96

Pages: 41-51

ISSN: 0010-4825

Publisher: Elsevier

Indexing

Scopus - 40 Citations

Other information

Authenticus ID: P-00N-QXF

DOI: 10.1016/j.compbiomed.2018.03.003

Abstract (EN): Breast cancer is the second leading cause of cancer death among women. Its early diagnosis is extremely important to prevent avoidable deaths. However, malignancy assessment of tissue biopsies is complex and dependent on observer subjectivity. Moreover, hematoxylin and eosin (H&E)-stained histological images exhibit a highly variable appearance, even within the same malignancy level. In this paper, we propose a computer-aided diagnosis (CAD) tool for automated malignancy assessment of breast tissue samples based on the processing of histological images. We provide four malignancy levels as the output of the system: normal, benign, in situ and invasive. The method is based on the calculation of three sets of features related to nuclei, colour regions and textures considering local characteristics and global image properties. By taking advantage of well-established image processing techniques, we build a feature vector for each image that serves as an input to an SVM (Support Vector Machine) classifier with a quadratic kernel. The method has been rigorously evaluated, first with a 5-fold cross-validation within an initial set of 120 images, second with an external set of 30 different images and third with images with artefacts included. Accuracy levels range from 75.8% when the 5-fold cross-validation was performed to 75% with the external set of new images and 61.11% when the extremely difficult images were added to the classification experiment. The experimental results indicate that the proposed method is capable of distinguishing between four malignancy levels with high accuracy. Our results are close to those obtained with recent deep learning-based methods. Moreover, it performs better than other state-of-the-art methods based on feature extraction, and it can help improve the CAD of breast cancer. © 2018 Elsevier Ltd

Language: English

Type (Professor's evaluation): Scientific