Advanced Severity Detection in Histopathological Ovarian Cancer: Rank-Based Leaf Wind Optimization and Alpha Piecewise Linear Fuzzy Techniques.

IF 2.1 4区医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL

Cancer Biotherapy and Radiopharmaceuticals Pub Date : 2025-10-06 DOI:10.1177/10849785251380023

Venkata Lakshmi S, Chandaka Pavan Sathish, Uma Pyla, Sravani K, Jayasree Pinajala, Nitalaksheswara Rao Kolukula, James Stephen Meka

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引用次数: 0

Abstract

Background: Ovarian cancer (OC) often goes undetected until advanced stages due to mild early symptoms. Methods: This research proposes a novel methodology for assessing OC severity through histopathological image analysis, utilizing Rank-Based Leaf in Wind Optimization and Alpha Piecewise Linear Fuzzy techniques. It enhances tissue image quality through normalization and Contrast Limited Adaptive Histogram Equalization, employs ResNet 50 with Inception v4 for feature extraction, and uses a ranking layer to prioritize key features. Results: The model achieved 99.25% accuracy and 97.98% precision, effectively classifying tumor severity levels under diagnostic uncertainty. Conclusion: This robust approach enhances diagnostic accuracy, supports early detection, and improves treatment planning. Future work will explore cross-validation, model pruning, and real-time integration for clinical applications.

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组织病理学卵巢癌的高级严重程度检测：基于秩的叶风优化和α分段线性模糊技术。

背景：由于早期症状轻微，卵巢癌（OC）往往直到晚期才被发现。方法：本研究提出了一种通过组织病理学图像分析评估OC严重程度的新方法，利用基于秩的Leaf in Wind优化和Alpha分段线性模糊技术。它通过归一化和对比度有限的自适应直方图均衡化来增强组织图像质量，使用ResNet 50和Inception v4进行特征提取，并使用排序层对关键特征进行优先级排序。结果：该模型准确率为99.25%，精密度为97.98%，能在诊断不确定的情况下对肿瘤严重程度进行有效分类。结论：这种稳健的方法提高了诊断的准确性，支持早期发现，并改善了治疗计划。未来的工作将探索交叉验证、模型修剪和临床应用的实时集成。

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来源期刊

Cancer Biotherapy and Radiopharmaceuticals 医学-核医学

CiteScore

7.80

自引率

2.90%

发文量

审稿时长

3 months

期刊介绍： Cancer Biotherapy and Radiopharmaceuticals is the established peer-reviewed journal, with over 25 years of cutting-edge content on innovative therapeutic investigations to ultimately improve cancer management. It is the only journal with the specific focus of cancer biotherapy and is inclusive of monoclonal antibodies, cytokine therapy, cancer gene therapy, cell-based therapies, and other forms of immunotherapies. The Journal includes extensive reporting on advancements in radioimmunotherapy, and the use of radiopharmaceuticals and radiolabeled peptides for the development of new cancer treatments.