Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia; Department of Computer Science, Sukkur IBA University, Sukkur, Pakistan.
Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia.
Comput Med Imaging Graph. 2021 Apr;89:101870. doi: 10.1016/j.compmedimag.2021.101870. Epub 2021 Jan 27.
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.
在过去的几年中，全球癌症负担急剧增加。在所有女性癌症类型中，乳腺癌（BrC）是导致非自然死亡的主要原因。为了进行早期诊断，组织病理学（Hp）成像相对于乳房 X 光照片而言，是对乳腺肿瘤（BrT）进行阳性和详细（在组织水平上）诊断的金标准。大量研究使用 BrT Hp 图像来解决使用高计算资源的二进制或多分类问题。然而，分类模型的性能可能会受到 Hp 图像中各种类型的 BrT 之间高度相关性的影响，这会导致错误分类率增加。因此，本文旨在通过深度学习（DL）开发基于树的 BrT 多分类模型，以提取判别特征，使用较少的计算资源解决多分类问题，从而提高性能。这项工作的主要贡献在于创建一个基于集合的、基于树的 DL 模型，该模型在 BreakHis 数据集上进行预训练，并实现一种错误分类减少算法。基于集合的、基于树的 DL 模型从 Hp 图像中提取判别性 BrT 特征。目标数据集（即 2015 年生物成像挑战赛乳腺组织学）规模较小；因此，为避免所提出模型的过拟合，在 BreakHis 数据集上进行预训练。而错误分类减少算法则用于增强分类模型的性能。实验结果表明，所提出的模型优于现有的最先进的基线研究。对于四种 BrT 亚型，所达到的分类准确率范围为 87.50%至 100%。因此，该模型可以作为任何医疗中心的医生的第二意见。
