CoMHisP: A Novel Feature Extractor for Histopathological Image Classification Based on Fuzzy SVM With Within-Class Relative Density

• Published in: IEEE transactions on fuzzy systems Vol. 29; no. 1; pp. 103 - 117
• Main Authors: Kumar, Abhinav, Singh, Sanjay Kumar, Saxena, Sonal, Singh, Amit Kumar, Shrivastava, Sameer, Lakshmanan, K., Kumar, Neeraj, Singh, Raj Kumar
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-01-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Breast cancer; Canine mammary tumors (CMTs); center of mass (CoM); Classifiers; Data analysis; Datasets; Density; Fault tolerance; feature engineering; Feature extraction; fuzzy membership; fuzzy support vector machine with relative density; histopathological image classification; Image analysis; Image classification; Image enhancement; Machine learning; Medical imaging; Micropatterning; Pattern recognition; Performance evaluation; Robustness (mathematics); Support vector machines; Tumors; Uncertainty
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2020.2995968

Machine learning (ML) has emerged as a powerful tool for pattern recognition. Traditional ML algorithms have limited ability to reveal the most sophisticated features of cancer histopathological images, but their robustness and fault tolerance can be enhanced by using fuzzy modeling to capture the uncertainty in image data. Therefore, this article proposes a novel CoMHisP framework based on a fuzzy support vector machine with within-class density information (FSVM-WD). It utilizes a novel feature extraction technique by optimizing the block size to extract image micropatterns and computing center of mass (CoM) for each pixel to extract feature vectors. The performance of the proposed framework is evaluated using a CMTHis dataset comprising histopathological images of canine mammary tumor (CMT), a prevalent neoplastic disease in female dogs, and an established model for human breast cancer. Data analysis reveals that stain normalization and magnification influence the performance of the CoMHisP framework, with the best results achieved at lower magnifications after stain normalization. The proposed framework achieves a classification accuracy of 97.25% (<inline-formula><tex-math notation="LaTeX">\pm</tex-math></inline-formula>1.80%) using a FSVM-WD classifier, outperforming both traditional ML and deep FE-VGGNET16-based feature descriptors. To the best of our knowledge, this is the first time a CoM-based feature descriptor has been proposed for histopathological image analysis of CMTs and its performance was evaluated using a fuzzy SVM-based classifier. The proposed method performs well with datasets of limited size and low-magnification images and, therefore, has the potential to provide rapid and accurate diagnosis in low-cost clinical settings.