Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms.

IF 3.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Rakesh Salakapuri, Panduranga Vital Terlapu, Kishore Raju Kalidindi, Ramesh Naidu Balaka, D Jayaram, T Ravikumar
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引用次数: 0

Abstract

Brain tumours (BTs) are severe neurological disorders. They affect more than 308,000 people each year worldwide. The mortality rate is over 251,000 deaths annually (IARC, 2020 reports). Detecting BTs is complex because they vary in nature. Early diagnosis is essential for better survival rates. The study presents a new system for detecting BTs. It combines deep (DL) learning and machine (ML) learning techniques. The system uses advanced models like Inception-V3, ResNet-50, and VGG-16 for feature extraction, and for dimensional reduction, it uses the PCA model. It also employs ensemble methods such as Stacking, k-NN, Gradient Boosting, AdaBoost, Multi-Layer Perceptron (MLP), and Support Vector Machines for classification and predicts the BTs using MRI scans. The MRI scans were resized to 224 × 224 pixels, and pixel intensities were normalized to a [0,1] scale. We apply the Gaussian filter for stability. We use the Keras Image Data Generator for image augmentation. It applied methods like zooming and ± 10% brightness adjustments. The dataset has 5,712 MRI scans. These scans are classified into four groups: Meningioma, No-Tumor, Glioma, and Pituitary. A tenfold cross-validation method helps check if the model is reliable. Deep transfer (TL) learning and ensemble ML models work well together. They showed excellent results in detecting BTs. The stacking ensemble model achieved the highest accuracy across all feature extraction methods, with ResNet-50 features reduced by PCA (500), producing an accuracy of 0.957, 95% CI: 0.948-0.966; AUC: 0.996, 95% CI: 0.989-0.998, significantly outperforming baselines (p < 0.01). Neural networks and gradient-boosting models also show strong performance. The stacking model is robust and reliable. This method is useful for medical applications. Future studies will focus on using multi-modal imaging. This will help improve diagnostic accuracy. The research improves early detection of brain tumors.

使用混合微调深度转移特征和集成机器学习算法的智能脑肿瘤检测。
脑肿瘤是一种严重的神经系统疾病。全球每年有超过308,000人受其影响。每年的死亡率超过251,000人(国际癌症研究机构,2020年报告)。检测bt很复杂,因为它们的性质各不相同。早期诊断对提高生存率至关重要。本研究提出了一种新的bt检测系统。它结合了深度(DL)学习和机器(ML)学习技术。该系统采用Inception-V3、ResNet-50、VGG-16等高级模型进行特征提取,采用PCA模型进行降维。它还采用集成方法,如堆叠、k-NN、梯度增强、AdaBoost、多层感知器(MLP)和支持向量机进行分类,并使用MRI扫描预测bt。MRI扫描调整为224 × 224像素,像素强度归一化为[0,1]尺度。我们使用高斯滤波器来保持稳定性。我们使用Keras图像数据生成器进行图像增强。它采用了变焦和±10%亮度调整等方法。该数据集有5,712个MRI扫描。这些扫描分为四组:脑膜瘤、无瘤、胶质瘤和垂体。十倍交叉验证方法有助于检查模型是否可靠。深度迁移(TL)学习和集成ML模型可以很好地协同工作。他们在检测bt方面取得了优异的成绩。在所有特征提取方法中,叠加集成模型的准确率最高,PCA(500)减少了ResNet-50特征,准确率为0.957,95% CI: 0.948-0.966;AUC: 0.996, 95% CI: 0.989-0.998,显著优于基线(p
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来源期刊
Scientific Reports
Scientific Reports Natural Science Disciplines-
CiteScore
7.50
自引率
4.30%
发文量
19567
审稿时长
3.9 months
期刊介绍: We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.
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