Gao Yan, Vali Mahsa
School of Electrical and Mechanical Engineering, Xuchang University, Xuchang, 461000, Henan, China.
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
J Imaging Inform Med. 2024 Dec 11. doi: 10.1007/s10278-024-01363-9.
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.
前列腺癌是最常见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。评估疾病严重程度涉及分析组织学组织样本，这些样本根据病理特征从1级（健康）到5级（严重恶性）进行分级。然而，传统的人工分级劳动强度大且容易出现差异。本研究通过提出一种新颖的组织学分级分析方法，应对了前列腺癌分类自动化的挑战。该方法将用于提取纹理特征的灰度共生矩阵（GLCM）与哈尔小波修正相结合，以提高特征质量。然后采用卷积神经网络（CNN）进行稳健分类。使用统计和性能指标对所提出的方法进行了评估，平均准确率达到97.3%，精确率为98%，曲线下面积（AUC）为0.95。这些结果强调了该方法在准确分类前列腺组织分级方面的有效性。本研究证明了自动化分类方法在支持病理学家、提高诊断精度以及改善前列腺癌护理临床结果方面的潜力。
