ContourTL-Net:基于轮廓的转移学习算法,用于早期脑肿瘤检测。

IF 3.3 Q2 ENGINEERING, BIOMEDICAL International Journal of Biomedical Imaging Pub Date : 2024-04-29 eCollection Date: 2024-01-01 DOI:10.1155/2024/6347920
N I Md Ashafuddula, Rafiqul Islam
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引用次数: 0

摘要

脑肿瘤是一种严重的神经系统疾病,由大脑或头骨中不受控制的细胞生长引起,通常会导致死亡。随着患者寿命的延长,需要及时发现;然而,脑组织的复杂性使得早期诊断具有挑战性。因此,需要自动化工具来帮助医护人员。本研究尤其旨在通过深度学习模型提高临床环境中计算机化脑肿瘤检测的效率。因此,本研究提出了一种新型的基于阈值的磁共振成像图像分割方法和基于轮廓的迁移学习模型(ContourTL-Net),以促进脑部恶性肿瘤的初期临床检测。该模型利用基于轮廓的分析,这对物体检测、精确分割和捕捉肿瘤形态的细微变化至关重要。该模型采用 VGG-16 架构,事先在 "ImageNet "集合上进行了特征提取和分类训练。该模型旨在利用其 10 个不可训练卷积层、3 个可训练卷积层和 3 个剔除层。所提出的 ContourTL-Net 模型在两个基准数据集上以四种方式进行了评估,其中未见病例被视为临床方面。在未见数据上验证深度学习模型对于确定模型的泛化能力、领域适应性、鲁棒性和实际应用性至关重要。在这里,所介绍模型的结果表明,对未见数据的分类非常准确,灵敏度和阴性预测值(NPV)均为 100%,特异性为 98.60%,精确度为 99.12%,F1 分数为 99.56%,准确率为 99.46%。此外,还将建议模型的结果与最先进的方法进行了比较,以进一步提高其有效性。建议的解决方案在可见数据和未见数据方面都优于现有解决方案,有望显著提高脑肿瘤检测效率和准确性,从而提早诊断并改善患者预后。
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ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection.

Brain tumors are critical neurological ailments caused by uncontrolled cell growth in the brain or skull, often leading to death. An increasing patient longevity rate requires prompt detection; however, the complexities of brain tissue make early diagnosis challenging. Hence, automated tools are necessary to aid healthcare professionals. This study is particularly aimed at improving the efficacy of computerized brain tumor detection in a clinical setting through a deep learning model. Hence, a novel thresholding-based MRI image segmentation approach with a transfer learning model based on contour (ContourTL-Net) is suggested to facilitate the clinical detection of brain malignancies at an initial phase. The model utilizes contour-based analysis, which is critical for object detection, precise segmentation, and capturing subtle variations in tumor morphology. The model employs a VGG-16 architecture priorly trained on the "ImageNet" collection for feature extraction and categorization. The model is designed to utilize its ten nontrainable and three trainable convolutional layers and three dropout layers. The proposed ContourTL-Net model is evaluated on two benchmark datasets in four ways, among which an unseen case is considered as the clinical aspect. Validating a deep learning model on unseen data is crucial to determine the model's generalization capability, domain adaptation, robustness, and real-world applicability. Here, the presented model's outcomes demonstrate a highly accurate classification of the unseen data, achieving a perfect sensitivity and negative predictive value (NPV) of 100%, 98.60% specificity, 99.12% precision, 99.56% F1-score, and 99.46% accuracy. Additionally, the outcomes of the suggested model are compared with state-of-the-art methodologies to further enhance its effectiveness. The proposed solution outperforms the existing solutions in both seen and unseen data, with the potential to significantly improve brain tumor detection efficiency and accuracy, leading to earlier diagnoses and improved patient outcomes.

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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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