Zahid Ullah, Mona Jamjoom, Manikandan Thirumalaisamy, Samah H Alajmani, Farrukh Saleem, Akbar Sheikh-Akbari, Usman Ali Khan
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Thus, convolution neural network (CNN) is a commonly utilized methodology developed for detecting various diseases from medical images because it is capable of extracting distinct features from an image under investigation. In this study, a deep learning approach is utilized to extricate distinct features from brain images in order to detect BT. Hence, CNN from scratch and transfer learning models (VGG-16, VGG-19, and LeNet-5) are developed and tested on brain images to build an intelligent decision support system for detecting BT. Since deep learning models require large volumes of data, data augmentation is used to populate the existing dataset synthetically in order to utilize the best fit detecting models. Hyperparameter tuning was conducted to set the optimum parameters for training the models. The achieved results show that VGG models outperformed others with an accuracy rate of 99.24%, average precision of 99%, average recall of 99%, average specificity of 99%, and average <i>f</i>1-score of 99% each. The results of the proposed models compared to the other state-of-the-art models in the literature show better performance of the proposed models in terms of accuracy, sensitivity, specificity, and <i>f</i>1-score. 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Hence, CNN from scratch and transfer learning models (VGG-16, VGG-19, and LeNet-5) are developed and tested on brain images to build an intelligent decision support system for detecting BT. Since deep learning models require large volumes of data, data augmentation is used to populate the existing dataset synthetically in order to utilize the best fit detecting models. Hyperparameter tuning was conducted to set the optimum parameters for training the models. The achieved results show that VGG models outperformed others with an accuracy rate of 99.24%, average precision of 99%, average recall of 99%, average specificity of 99%, and average <i>f</i>1-score of 99% each. The results of the proposed models compared to the other state-of-the-art models in the literature show better performance of the proposed models in terms of accuracy, sensitivity, specificity, and <i>f</i>1-score. 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引用次数: 0
摘要
脑肿瘤(BT)是一种可怕的疾病,也是导致人类死亡的首要原因之一。脑肿瘤的发展主要分为两个阶段,体积、形态和结构各不相同,可以通过化疗、放疗和外科手术等特殊临床程序治愈。过去几年,随着放射组学和医学影像研究的革命性进展,计算机辅助诊断系统(CAD),尤其是深度学习,在各种疾病的自动检测和诊断中发挥了关键作用,极大地为医疗临床医生提供了准确的决策支持系统。因此,卷积神经网络(CNN)是从医学图像中检测各种疾病的常用方法,因为它能够从所调查的图像中提取独特的特征。本研究利用深度学习方法从大脑图像中提取不同的特征,以检测 BT。因此,本研究开发了从头开始的 CNN 和迁移学习模型(VGG-16、VGG-19 和 LeNet-5),并在脑图像上进行了测试,以建立检测 BT 的智能决策支持系统。由于深度学习模型需要大量数据,因此使用了数据扩增技术来合成现有数据集,以便利用最合适的检测模型。通过超参数调整,为训练模型设置了最佳参数。结果显示,VGG 模型的准确率为 99.24%,平均精确度为 99%,平均召回率为 99%,平均特异性为 99%,平均 f1 分数为 99%,均优于其他模型。与文献中其他最先进的模型相比,提出的模型在准确率、灵敏度、特异性和 f1 分数方面都有更好的表现。此外,对比分析表明,所提出的模型是可靠的,它们可以用于检测 BT,也可以帮助医疗从业人员诊断 BT。
A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor.
Brain tumor (BT) is an awful disease and one of the foremost causes of death in human beings. BT develops mainly in 2 stages and varies by volume, form, and structure, and can be cured with special clinical procedures such as chemotherapy, radiotherapy, and surgical mediation. With revolutionary advancements in radiomics and research in medical imaging in the past few years, computer-aided diagnostic systems (CAD), especially deep learning, have played a key role in the automatic detection and diagnosing of various diseases and significantly provided accurate decision support systems for medical clinicians. Thus, convolution neural network (CNN) is a commonly utilized methodology developed for detecting various diseases from medical images because it is capable of extracting distinct features from an image under investigation. In this study, a deep learning approach is utilized to extricate distinct features from brain images in order to detect BT. Hence, CNN from scratch and transfer learning models (VGG-16, VGG-19, and LeNet-5) are developed and tested on brain images to build an intelligent decision support system for detecting BT. Since deep learning models require large volumes of data, data augmentation is used to populate the existing dataset synthetically in order to utilize the best fit detecting models. Hyperparameter tuning was conducted to set the optimum parameters for training the models. The achieved results show that VGG models outperformed others with an accuracy rate of 99.24%, average precision of 99%, average recall of 99%, average specificity of 99%, and average f1-score of 99% each. The results of the proposed models compared to the other state-of-the-art models in the literature show better performance of the proposed models in terms of accuracy, sensitivity, specificity, and f1-score. Moreover, comparative analysis shows that the proposed models are reliable in that they can be used for detecting BT as well as helping medical practitioners to diagnose BT.