Abstract
Colorectal and prostate cancers are the most common types of cancer in men worldwide. To diagnose colorectal and prostate cancer, a pathologist performs a histological analysis on needle biopsy samples. This manual process is time-consuming and error-prone, resulting in high intra and interobserver variability, which affects diagnosis reliability. This study aims to develop an automatic computerized system for diagnosing colorectal and prostate tumors by using images of biopsy samples to reduce time and diagnosis error rates associated with human analysis. We propose a CNN model for classifying colorectal and prostate tumors from multispectral images of biopsy samples. The key idea was to remove the last block of the convolutional layers and halve the number of filters per layer. Our results showed excellent performance, with an average test accuracy of 99.8% and 99.5% for the prostate and colorectal data sets, respectively. The system showed excellent performance when compared with pretrained CNNs and other classification methods, as it avoids the preprocessing phase while using a single CNN model for classification. Overall, the proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images. The proposed CNN was detailed and compared with previously trained network models used as feature extractors. These CNNs were also compared with other classification techniques. As opposed to pretrained CNNs and other classification approaches, the proposed CNN yielded excellent results. The computational complexity of the CNNs was also investigated, it was shown that the proposed CNN is better at classifying images than pretrained networks because it does not require preprocessing. Thus, the overall analysis was that the proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images.
Abstract (translated)
colorectal和prostate cancer是全球范围内男性最常见的癌症类型。为了诊断 colorectal和prostate cancer,病理科医生会对穿刺样本进行组织学分析。这种方法手动处理时间较长且容易出错,导致内参和外参变量的高变异性,从而影响诊断的可靠性。 本研究旨在开发一种自动的电脑化系统以诊断 colorectal和prostate cancer,通过使用穿刺样本的图像来减少与人类分析相关的时间和诊断错误率。我们提出了卷积神经网络模型,以从穿刺样本的彩色图像中分类 colorectal和prostate肿瘤。关键是要删除卷积层的最后一块并减少每层中的滤波器数量。我们的结果显示表现非常好,其中prostate和 colorectal数据集的测试准确率分别为99.8%和99.5%。系统与其他预训练神经网络和分类方法相比表现非常好,因为它在分类的同时避免了预处理阶段。 overall, the proposed CNN architecture was globally the best-performing system for classification of colorectal and prostate cancer images. The proposed CNN was detailed and compared with previously trained network models used as feature extractors. These CNNs were also compared with other classification techniques. Unlike pretrained CNNs and other classification approaches, the proposed CNN resulted in excellent results. The computational complexity of the CNNs was also investigate, it was shown that the proposed CNN is better at classification than pretrained networks because it does not require preprocessing. Therefore, the overall analysis was that the proposed CNN architecture was globally the best-performing system for classification of colorectal and prostate cancer images.
URL
https://arxiv.org/abs/2301.13151
