基于自动多模态融合的超参数调谐深度学习脑肿瘤诊断模型

S. Sandhya, M. Senthil Kumar
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引用次数: 0

摘要

随着医学图像处理研究的不断深入,图像融合已经成为一种现实的解决方案,自动从许多图像中提取相关数据,然后将它们融合成一个统一的图像。计算机断层扫描(CT)、磁共振成像(MRI)等医学成像技术在脑肿瘤(BT)的诊断和分类中起着至关重要的作用。单一的影像技术不足以正确诊断本病。如果扫描结果不明确,可能会导致医生做出错误的诊断,这对病人来说可能是不安全的。解决这一问题的方法是融合来自不同扫描图像的互补信息,以最小的不确定性生成准确的图像。提出了一种基于多模态深度学习(AMDL-BTDC)的脑肿瘤自动识别与分类新方法。提出的AMDL-BTDC模型首先使用双边滤波(BF)技术进行图像预处理。接下来,使用一对称为EfficientNet和SqueezeNet的预训练深度学习模型生成特征向量。采用黏菌算法获取深度学习模型的最优超参数设置(SMA)。最后,将特征融合后的自编码器(AE)模型用于BT分类。在基准医学成像数据集上进行了广泛的测试,验证了该模型在不同度量下优于其他技术的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Multimodal Fusion Based Hyperparameter Tuned Deep Learning Model for Brain Tumor Diagnosis
As medical image processing research has progressed, image fusion has emerged as a realistic solution, automatically extracting relevant data from many images before fusing them into a single, unified image. Medical imaging techniques, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), etc., play a crucial role in the diagnosis and classification of brain tumors (BT). A single imaging technique is not sufficient for correct diagnosis of the disease. In case the scans are ambiguous, it can lead doctors to incorrect diagnoses, which can be unsafe to the patient. The solution to this problem is fusing images from different scans containing complementary information to generate accurate images with minimum uncertainty. This research presents a novel method for the automated identification and classification of brain tumors using multi-modal deep learning (AMDL-BTDC). The proposed AMDL-BTDC model initially performs image pre-processing using bilateral filtering (BF) technique. Next, feature vectors are generated using a pair of pre-trained deep learning models called EfficientNet and SqueezeNet. Slime Mold Algorithm is used to acquire the DL models’ optimal hyperparameter settings (SMA). In the end, an autoencoder (AE) model is used for BT classification once features have been fused. The suggested model’s superior performance over other techniques under diverse measures was validated by extensive testing on the benchmark medical imaging dataset.
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来源期刊
Journal of Medical Imaging and Health Informatics
Journal of Medical Imaging and Health Informatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas. As an example, the Distributed Diagnosis and Home Healthcare (D2H2) aims to improve the quality of patient care and patient wellness by transforming the delivery of healthcare from a central, hospital-based system to one that is more distributed and home-based. Different medical imaging modalities used for extraction of information from MRI, CT, ultrasound, X-ray, thermal, molecular and fusion of its techniques is the focus of this journal.
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