Poonkuzhali P, Krishnamoorthy R, Nimma Divya, Ramesh Janjhyam Venkata Naga
Department of ECE, R.M.D. Engineering College, Kavaraipettai, India.
Centre for Advanced Wireless Integrated Technology, Chennai Institute of Technology, Chennai, India.
Expert Rev Anticancer Ther. 2025 Jul;25(7):829-843. doi: 10.1080/14737140.2025.2512040. Epub 2025 May 28.
Prostate Cancer (PCa) is a severe disease that affects males globally. The Gleason grading system is a widely recognized method for diagnosing the aggressiveness of PCa using histopathological images. This system evaluates prostate tissue to determine the severity of the disease and guide treatment decisions. However, manual analysis of histopathological images requires highly skilled professionals and is time-consuming.
To address these challenges, deep learning (DL) is utilized, as it has shown promising results in medical image analysis. Although numerous DL networks have been developed for Gleason grading, many existing methods have limitations such as suboptimal accuracy and high computational complexity. The proposed network integrates MobileNet, an Attention Mechanism (AM), and a capsule network. MobileNet efficiently extracts features from images while addressing computational complexity. The AM focuses on selecting the most relevant features, enhancing the accuracy of Gleason grading. Finally, the capsule network classifies the Gleason grades from histopathological images.
The validation of the proposed network used two datasets, PANDA and Gleason-2019. Ablation studies were conducted and evaluated in the proposed architecture. The results highlight the effectiveness of the proposed network.
The proposed network outperformed existing approaches, achieving an accuracy of 98.08% on the PANDA dataset and 97.07% on the Gleason-2019 dataset.
前列腺癌（PCa）是一种影响全球男性的严重疾病。 Gleason分级系统是一种广泛认可的利用组织病理学图像诊断PCa侵袭性的方法。该系统评估前列腺组织以确定疾病的严重程度并指导治疗决策。然而，组织病理学图像的人工分析需要高技能的专业人员且耗时。
为应对这些挑战，采用了深度学习（DL），因为它在医学图像分析中已显示出有前景的结果。尽管已经开发了许多用于Gleason分级的DL网络，但许多现有方法存在局限性，如准确性欠佳和计算复杂度高。所提出的网络集成了MobileNet、注意力机制（AM）和胶囊网络。MobileNet在解决计算复杂度的同时有效地从图像中提取特征。AM专注于选择最相关的特征，提高Gleason分级的准确性。最后，胶囊网络从组织病理学图像中对Gleason分级进行分类。
所提出网络的验证使用了两个数据集，PANDA和Gleason - 2019。在所提出的架构中进行了消融研究并进行了评估。结果突出了所提出网络的有效性。
所提出的网络优于现有方法，在PANDA数据集上达到了98.08%的准确率，在Gleason - 2019数据集上达到了97.07%的准确率。
