Abstract
Cancer is a leading cause of death in many countries. An early diagnosis of cancer based on biomedical imaging ensures effective treatment and a better prognosis. However, biomedical imaging presents challenges to both clinical institutions and researchers. Physiological anomalies are often characterized by slight abnormalities in individual cells or tissues, making them difficult to detect visually. Traditionally, anomalies are diagnosed by radiologists and pathologists with extensive training. This procedure, however, demands the participation of professionals and incurs a substantial cost. The cost makes large-scale biological image classification impractical. In this study, we provide unique deep neural network designs for multiclass classification of medical images, in particular cancer images. We incorporated transformers into a multiclass framework to take advantage of data-gathering capability and perform more accurate classifications. We evaluated models on publicly accessible datasets using various measures to ensure the reliability of the models. Extensive assessment metrics suggest this method can be used for a multitude of classification tasks.
Abstract (translated)
癌症是许多国家的主要原因之一。基于生物医学成像的早期发现癌症能够保证有效的治疗方案和更好的预后。然而,生物医学成像对临床机构和研究人员都提出了挑战。生理异常通常表现为单个细胞或组织中的轻微异常,这使得它们很难通过视觉方法检测。传统上,异常是由具有广泛训练的医学影像学和病理学家进行诊断的。然而,这种方法需要专业人员的参与并产生大量成本。成本使得大规模生物图像分类不可行。在本研究中,我们提供了独特的深度学习网络设计,用于对医学图像的多分类分类,特别是癌症图像的分类。我们将transformers融入到多分类框架中,利用收集数据的能力并实现更准确的分类。我们使用各种测量方法对模型进行评估,以确保其可靠性。广泛的评估指标表明,这种方法可以用于多种分类任务。
URL
https://arxiv.org/abs/2301.09007
