基于迁移学习的优化ResNet152模型在MR图像中检测脑肿瘤

IF 6.3 2区 医学 Q1 BIOLOGY
Prabhpreet Kaur, Priyanka Mahajan
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引用次数: 0

摘要

脑瘤是非常有害的,可以大大降低预期寿命。大多数研究人员使用磁共振(MR)扫描来检测肿瘤,因为它们可以提供受影响区域的详细图像。最近,基于人工智能的深度学习方法已经出现,通过有效的数据处理来提高诊断的准确性。本研究探讨深度迁移学习技术对脑肿瘤准确诊断的有效性。预处理流水线用于提高图像质量。该管道包括形态学操作,如用于形状细化的侵蚀和膨胀,用于降噪的高斯模糊,以及用于图像裁剪的阈值设置。采用主成分分析(PCA)进行降维,数据增强丰富了数据集。数据集被划分为训练(80%)和测试(20%)。预训练的ResNet152和GoogleNet从图像中提取有意义的特征。然后使用传统的机器学习分类器对这些提取的特征进行分类:支持向量机(SVM)、k近邻(KNN)、分类和回归树(CART)和高斯朴素贝叶斯(GNB)。本研究比较了两种医学图像分析预训练模型的性能。准确度、灵敏度、召回率和F1-Score等性能指标评估最终的分类结果。ResNet152优于GoogleNet,准确率为98.53%,F1得分为97.4%,灵敏度为96.52%。本研究强调将深度学习和传统机器学习技术整合到医学图像分析中,以有效地检测脑肿瘤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of brain tumors using a transfer learning-based optimized ResNet152 model in MR images
Brain tumors are incredibly harmful and can drastically reduce life expectancy. Most researchers use magnetic resonance (MR) scans to detect tumors because they can provide detailed images of the affected area. Recently, AI-based deep learning methods have emerged to enhance diagnostic accuracy through efficient data processing. This study investigates the effectiveness of deep transfer learning techniques for accurate brain tumor diagnosis. A preprocessing pipeline is used to enhance the image quality. This pipeline includes morphological operations such as erosion and dilation for shape refinement, Gaussian blurring for noise reduction, and thresholding for image cropping. Principal Component Analysis (PCA) is applied for dimensionality reduction, and data augmentation enriches the dataset. The dataset is partitioned into training (80 %) and testing (20 %). Pretrained ResNet152 and GoogleNet extract meaningful features from the images. These extracted features are then classified using conventional machine learning classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), and Gaussian Naive Bayes (GNB). This study compares the performance of two pre-trained models for medical image analysis. Performance metrics such as accuracy, sensitivity, recall, and F1-Score evaluate the final classification results. ResNet152 outperforms GoogleNet, achieving a 98.53 % accuracy, an F1 score of 97.4 %, and a sensitivity of 96.52 %. This study highlights integrating deep learning and traditional machine-learning techniques in medical image analysis for effective brain tumor detection.
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来源期刊
Computers in biology and medicine
Computers in biology and medicine 工程技术-工程:生物医学
CiteScore
11.70
自引率
10.40%
发文量
1086
审稿时长
74 days
期刊介绍: Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.
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