A hybrid feature extraction based ensemble model for breast cancer detection and classification using different medical images

A.H.M. Zadidul Karim , Kazi Bil Oual Mahmud , Celia Shahnaz
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引用次数: 0

Abstract

Breast cancer is a fatal disease that has a high death rate worldwide, according to the WHO. Hence, implementing medical image-based automated breast cancer detection and classification is essential for early identification and categorization. It plays a crucial role in developing efficient treatment methods by accurately diagnosing the kind and classifying the subtype of breast cancer. Ultrasound and mammograms are primary and efficient methods for detection, whereas histopathology is an advanced method for exactly classifying breast cancer. Previously, different hand-engineered features were used for different types of data sets, respectively, which provided high accuracy individually. However, deep learning is a strong tool for computer vision tasks. Therefore, we developed a unique combination of hand-engineered features for color, shape, and texture extraction in parallel to three different deep neural networks. Such a hybrid method proposed that combines both hand-engineered and deep learning-based feature extractors provides an outstanding performance for breast cancer detection and classification on different types of datasets compared to the state of the art methods thus verifying its robustness and effectiveness.

Abstract Image

基于混合特征提取的集成模型用于不同医学图像的乳腺癌检测与分类
据世界卫生组织称,乳腺癌是一种致命的疾病,在世界范围内具有很高的死亡率。因此,实现基于医学图像的乳腺癌自动检测和分类对于早期识别和分类至关重要。准确诊断乳腺癌的种类和亚型,对制定有效的治疗方法起着至关重要的作用。超声和乳房x光检查是主要和有效的检测方法,而组织病理学是准确分类乳腺癌的先进方法。以前,不同类型的数据集分别使用不同的手工工程特征,单独提供较高的精度。然而,深度学习是计算机视觉任务的强大工具。因此,我们开发了一种独特的手工设计特征组合,用于颜色、形状和纹理提取,并与三种不同的深度神经网络并行。与现有方法相比,这种结合了手工设计和基于深度学习的特征提取器的混合方法在不同类型的数据集上提供了出色的乳腺癌检测和分类性能,从而验证了其鲁棒性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Intelligence-based medicine
Intelligence-based medicine Health Informatics
CiteScore
5.00
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审稿时长
187 days
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