{"title":"基于迁移学习的优化ResNet152模型在MR图像中检测脑肿瘤","authors":"Prabhpreet Kaur, Priyanka Mahajan","doi":"10.1016/j.compbiomed.2025.109790","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"188 ","pages":"Article 109790"},"PeriodicalIF":6.3000,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection of brain tumors using a transfer learning-based optimized ResNet152 model in MR images\",\"authors\":\"Prabhpreet Kaur, Priyanka Mahajan\",\"doi\":\"10.1016/j.compbiomed.2025.109790\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":10578,\"journal\":{\"name\":\"Computers in biology and medicine\",\"volume\":\"188 \",\"pages\":\"Article 109790\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2025-02-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers in biology and medicine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0010482525001404\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in biology and medicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010482525001404","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
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.
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.