通过图像提取和机器学习增强骨癌诊断:一种最新的方法。

IF 1.2 4区 医学 Q3 SURGERY
Surgical Innovation Pub Date : 2024-02-01 Epub Date: 2023-12-07 DOI:10.1177/15533506231220968
Abhishek Shrivastava, Mukesh Kumar Nag
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引用次数: 0

摘要

背景:骨癌是一种严重的疾病,经常导致患者死亡。诊断依赖于x射线、核磁共振成像或CT扫描,这些都需要专家耗时的人工审查。因此,开发一个自动化系统对于准确分类恶性骨和健康骨至关重要。方法:区分它们是一个挑战,因为它们可能表现出相似的物理特征。首先选择最优的边缘检测方法。然后生成两个特征集:一个具有定向梯度直方图(HOG),另一个没有。性能评估涉及两种机器学习模型:支持向量机(SVM)和随机森林。结果:包括HOG始终产生优越的结果。HOG支持向量机模型的F-1得分为0.92,优于随机森林模型的0.77。本研究旨在建立可靠的骨癌分类方法。提出的自动化方法帮助外科医生使用现代图像分析技术和机器学习模型准确检测恶性骨区域。结合HOG可显著提高生产性能,改善恶性骨与健康骨的分化。结论:最终,该方法支持骨癌患者的精确诊断和明智的治疗决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach.

Background: Bone cancer is a severe condition often leading to patient mortality. Diagnosis relies on X-rays, MRIs, or CT scans, which require time-consuming manual review by experts. Thus, developing an automated system is crucial for accurate classification of malignant and healthy bone.Methods: Differentiating between them poses a challenge as they may exhibit similar physical characteristics. The initial step is selecting the optimal edge detection method. Two feature sets are then generated: one with the histogram of oriented gradients (HOG) and one without. Performance evaluation involves two machine learning models: Support Vector Machine (SVM) and Random Forest.Results: Including HOG consistently yields superior results. The SVM model with HOG achieves an F-1 score of 0.92, outperforming the Random Forest model's .77. This study aims to develop reliable methods for bone cancer classification. The proposed automated method assists surgeons in accurately detecting malignant bone regions using modern image analysis techniques and machine learning models. Incorporating HOG significantly enhances performance, improving differentiation between malignant and healthy bone.Conclusion: Ultimately, this approach supports precise diagnoses and informed treatment decisions for bone cancer patients.

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来源期刊
Surgical Innovation
Surgical Innovation 医学-外科
CiteScore
2.90
自引率
0.00%
发文量
72
审稿时长
6-12 weeks
期刊介绍: Surgical Innovation (SRI) is a peer-reviewed bi-monthly journal focusing on minimally invasive surgical techniques, new instruments such as laparoscopes and endoscopes, and new technologies. SRI prepares surgeons to think and work in "the operating room of the future" through learning new techniques, understanding and adapting to new technologies, maintaining surgical competencies, and applying surgical outcomes data to their practices. This journal is a member of the Committee on Publication Ethics (COPE).
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