A tree-based multiclassification of breast tumor histopathology images through deep learning. (April 2021)

• Record Type: Journal Article
• Authors: Murtaza, Ghulam; Abdul Wahab, Ainuddin Wahid; Raza, Ghulam; Shuib, Liyana
• Is Part Of: Computerized medical imaging and graphics. Volume 89(2021)
• Publication Date: 2021-04
• Subjects: Breast cancer -- Breast tumor classification -- Histopathology image -- Deep learning -- Ensemble classification model; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2021.101870
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: A tree-based deep learning model for breast tumor multiclassification. Model can be used as 2ndopinion for the diagnosis of breast tumor 4 subtypes. Proposed model is computationally feasible, trained on normal desktop computer. Model shown better results, compared to the stat-of-the-art existing models. Abstract: Worldwide, the burden of cancer is drastically increasing over the past few years. Among all types of cancers in women, breast cancer (BrC) is the main cause of unnatural deaths. For early diagnosis, histopathology (Hp) imaging is a gold standard for positive and detailed (at tissue level) diagnosis of breast tumor (BrT) compared to mammogram images. A large number of studies used BrT Hp images to solve binary or multiclassification problems using high computational resources. However, classification models' performance may be compromised due to the high correlation among various types of BrT in Hp images, which raises the misclassification rate. Thus, this paper aims to develop a tree-based BrT multiclassification model via deep learning (DL) to extract discriminative features to solve the multiclassification problem with better performance using less computational resources. The main contributions of this work are to create an ensemble, tree-based DL model that is pre-trained on the BreakHis dataset, and implementation of a misclassification reduction algorithm. The ensemble, tree-based DL model, extracts discriminative BrT features from Hp images. The target dataset (i.e., Bioimaging challenge 2015 breast histology) is small in size; thus, to avoid overfitting of the proposed model, pretraining is performed on the BreakHis dataset. Whereas, misclassification reduction algorithm is implemented to enhance the performance of the classification model. The experimental results show that the proposed model outperformed the existing state-of-the-art baseline studies. The achieved classification accuracy is ranging from 87.50 % to 100 % for four subtypes of BrT. Thus, the proposed model can assist doctors as the second opinion in any healthcare centre.