WITHDRAWN: Histomorphometric Image Classifier of Different Grades of Oral Squamous Cell Carcinoma Using Transfer Learning and Convolutional Neural Network

IF 2.2 3区医学 Q2 Dentistry

Journal of Stomatology Oral and Maxillofacial Surgery Pub Date : 2024-04-16 DOI:10.1016/j.jormas.2024.101876

Dr Ayushi Jain, Nitika Gupta, Dr Pooja Sharma, Dr Om Prakash Gupta, Dr Shalini Gupta, Dr Amaresh Kumar Sahoo

引用次数: 0

Abstract

This article has been withdrawn at the request of the authors. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal

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利用迁移学习和卷积神经网络对不同分级的口腔鳞状细胞癌进行组织形态学图像分类

背景：机器学习是医疗保健领域的一项新兴技术，旨在从根本上改革传统系统，为医疗从业人员提供帮助。口腔癌的组织病理学分析对于病理学家确定其分级至关重要。因此，本研究尝试使用不同的深度学习模型对 OSCC（口腔鳞状细胞癌）患者的各种染色组织样本进行分级：收集了 120 张 OSCC 组织病理学图像数据集，并根据 Broders 的分级系统将其分为分化良好（40 张）、中度分化（40 张）和分化不良（40 张）。CNN（卷积神经网络）架构基于预先训练的神经网络模型VGG16（视觉几何组16）、VGG19（视觉几何组19）、RESNET50（残差网络50）和DENSENET121（密集网络121）模型进行图像分析：在放大 4 倍的情况下，所有四个模型都达到了最高的测试准确率，即 66.67%。DENSENET121 的验证准确率最高，达到 81%。在 10 倍放大率下，RESNET50、VGG19 和 DENSENET121 的测试准确率最高，达到 90.9%，VGG19 的验证准确率最高，达到 83.3%。在 40x 时，RESNET50 和 DENSENET121 的测试准确率最高，达到 70%。RESNET50、VGG16和VGG19的验证准确率相当：在数字成像和计算辅助诊断中借助深度学习对组织进行分级，有助于对口腔癌患者进行及时有效的预后判断和多模式治疗方案，从而减轻病理学家的业务工作量。通过系统评估模型性能和解决过拟合问题，我们开发出了适用于医疗诊断的稳健模型。

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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来源期刊

Journal of Stomatology Oral and Maxillofacial Surgery Dentistry-Oral Surgery

CiteScore

2.20

自引率

9.10%

发文量

305

期刊介绍： J Stomatol Oral Maxillofac Surg publishes research papers and techniques - (guest) editorials, original articles, reviews, technical notes, case reports, images, letters to the editor, guidelines - dedicated to enhancing surgical expertise in all fields relevant to oral and maxillofacial surgery: from plastic and reconstructive surgery of the face, oral surgery and medicine, … to dentofacial and maxillofacial orthopedics. Original articles include clinical or laboratory investigations and clinical or equipment reports. Reviews include narrative reviews, systematic reviews and meta-analyses. All manuscripts submitted to the journal are subjected to peer review by international experts, and must: Be written in excellent English, clear and easy to understand, precise and concise; Bring new, interesting, valid information - and improve clinical care or guide future research; Be solely the work of the author(s) stated; Not have been previously published elsewhere and not be under consideration by another journal; Be in accordance with the journal''s Guide for Authors'' instructions: manuscripts that fail to comply with these rules may be returned to the authors without being reviewed. Under no circumstances does the journal guarantee publication before the editorial board makes its final decision. The journal is indexed in the main international databases and is accessible worldwide through the ScienceDirect and ClinicalKey Platforms.