Combination of Deep and Statistical Features of the Tissue of Pathology Images to Classify and Diagnose the Degree of Malignancy of Prostate Cancer.

Yan Gao, Mahsa Vali
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Abstract

Prostate cancer is one of the most prevalent male-specific diseases, where early and accurate diagnosis is essential for effective treatment and preventing disease progression. Assessing disease severity involves analyzing histological tissue samples, which are graded from 1 (healthy) to 5 (severely malignant) based on pathological features. However, traditional manual grading is labor-intensive and prone to variability. This study addresses the challenge of automating prostate cancer classification by proposing a novel histological grade analysis approach. The method integrates the gray-level co-occurrence matrix (GLCM) for extracting texture features with Haar wavelet modification to enhance feature quality. A convolutional neural network (CNN) is then employed for robust classification. The proposed method was evaluated using statistical and performance metrics, achieving an average accuracy of 97.3%, a precision of 98%, and an AUC of 0.95. These results underscore the effectiveness of the approach in accurately categorizing prostate tissue grades. This study demonstrates the potential of automated classification methods to support pathologists, enhance diagnostic precision, and improve clinical outcomes in prostate cancer care.

结合病理图像组织的深度特征和统计特征对前列腺癌恶性程度进行分类和诊断。
前列腺癌是最普遍的男性特有疾病之一,早期准确诊断对于有效治疗和预防疾病进展至关重要。评估疾病严重程度包括分析组织学组织样本,根据病理特征从1(健康)到5(严重恶性)分级。然而,传统的人工分级是劳动密集型的,而且容易发生变化。本研究通过提出一种新的组织学分级分析方法来解决自动化前列腺癌分类的挑战。该方法将灰度共生矩阵(GLCM)提取纹理特征与Haar小波变换相结合,提高了纹理特征的质量。然后采用卷积神经网络(CNN)进行鲁棒分类。采用统计学和性能指标对该方法进行了评估,平均准确度为97.3%,精密度为98%,AUC为0.95。这些结果强调了该方法在准确分类前列腺组织等级方面的有效性。这项研究证明了自动分类方法的潜力,以支持病理学家,提高诊断精度,并改善前列腺癌护理的临床结果。
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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