Isolated Convolutional-Neural-Network-Based Deep-Feature Extraction for Brain Tumor Classification Using Shallow Classifier.
Isolated Convolutional-Neural-Network-Based Deep-Feature Extraction for Brain Tumor Classification Using Shallow Classifier.
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DOI：
10.3390/diagnostics12081793
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发表时间：
2022-07-24
期刊：
影响因子：
--
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在当今世界，脑瘤是最严重的疾病之一。如果在晚期被发现，可能会导致非常有限的存活率。因此，脑肿瘤的分类对于制定合理的治疗计划，提高患者的生活质量至关重要。本研究使用经典/线性机器学习分类器(MLCs)来研究深度特征训练的脑肿瘤检测和分化模型。在这项研究中，转移学习被用来从构造的卷积神经网络(CNN)中获得深部脑磁共振成像(MRI)扫描特征。首先，构建和训练多层(19、22和25)孤立的CNN以评估性能。然后利用开发的CNN模型通过转移学习提取深层特征来训练多个MLC。利用已有的脑MRI数据集对该方法进行了验证。此外，还提取了预训练模型的深层特征，以评估和比较该方法的性能。所提出的CNN深度特征训练支持向量机模型比其他常用的预先训练的深度特征MLC训练模型具有更高的准确率。该方法对脑肿瘤的检测和识别准确率为98%。对于未用于训练模型的未知数据集，该算法也具有较好的分类正确率(97.2%)。经过广泛的测试和分析，建议的技术可能有助于帮助医生诊断脑肿瘤。
In today’s world, a brain tumor is one of the most serious diseases. If it is detected at an advanced stage, it might lead to a very limited survival rate. Therefore, brain tumor classification is crucial for appropriate therapeutic planning to improve patient life quality. This research investigates a deep-feature-trained brain tumor detection and differentiation model using classical/linear machine learning classifiers (MLCs). In this study, transfer learning is used to obtain deep brain magnetic resonance imaging (MRI) scan features from a constructed convolutional neural network (CNN). First, multiple layers (19, 22, and 25) of isolated CNNs are constructed and trained to evaluate the performance. The developed CNN models are then utilized for training the multiple MLCs by extracting deep features via transfer learning. The available brain MRI datasets are employed to validate the proposed approach. The deep features of pre-trained models are also extracted to evaluate and compare their performance with the proposed approach. The proposed CNN deep-feature-trained support vector machine model yielded higher accuracy than other commonly used pre-trained deep-feature MLC training models. The presented approach detects and distinguishes brain tumors with 98% accuracy. It also has a good classification rate (97.2%) for an unknown dataset not used to train the model. Following extensive testing and analysis, the suggested technique might be helpful in assisting doctors in diagnosing brain tumors.
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通过自适应空间池和 Fisher 向量表示检索脑肿瘤
影响因子：
3.7
作者：
Cheng J;Yang W;Huang M;Huang W;Jiang J;Zhou Y;Yang R;Zhao J;Feng Y;Feng Q;Chen W
通讯作者：
Chen W
DOI：
10.1007/s40998-021-00426-9
发表时间：
2021-04-22
期刊：
Iranian Journal of Science and Technology, Transactions of Electrical Engineering
影响因子：
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作者：
Irmak E
通讯作者：
Irmak E
DOI：
10.3390/healthcare10030494
发表时间：
2022-03-08
期刊：
Healthcare (Basel, Switzerland)
影响因子：
--
作者：
Ait Amou M;Xia K;Kamhi S;Mouhafid M
通讯作者：
Mouhafid M
影响因子：
7.5
作者：
CORTES, C;VAPNIK, V
通讯作者：
VAPNIK, V
影响因子：
3.2
作者：
Ali, Muhammad Umair;Zafar, Amad;Kim, Hee-Je
通讯作者：
Kim, Hee-Je