整合MobileNetV3和SqueezeNet用于多类脑瘤分类
Sahithi Kantu1, Hema Sai Kaja1, Vaishnavi Kukkala1
1Department of Electrical & Computer Engineering and Computer Science, University of New Haven, West Haven, CT, USA.
Journal of imaging informatics in medicine
|July 3, 2025
概括
这项研究介绍了使用MRI扫描进行脑瘤分类的轻量级深度学习模型. 移动NetV3实现了99.31%的准确性,为诊断质瘤,脑膜瘤和垂体瘤提供了有效的解决方案.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 计算神经科学是一种神经科学.
背景情况:
- 准确的脑瘤分类对于有效治疗至关重要.
- 传统的MRI分析是耗时且主观的.
- 需要自动化分类方法来提高效率和一致性.
研究的目的:
- 评估用于多类脑瘤分类的轻量级深度学习模型.
- 为了比较MobileNetV3,SqueezeNet和混合模型的性能.
- 评估准确性和计算效率之间的权衡.
主要方法:
- 利用了7023张MRI图像的数据集,用于四个类别:质瘤,脑膜瘤,垂体瘤和没有瘤.
- 研究了单个和功能融合的MobileNetV3和SqueezeNet模型.
- 采用Grad-CAM用于模型解释性和可视化.
主要成果:
- 移动NetV3仅用3.47M个参数实现了最高的测试准确率99.31%,达到3.47M个参数.
- 拟议的轻量级模型的性能优于VGG16和InceptionV3.3等基线架构.
- 格拉德-CAM可视化有效地突出了瘤特定区域.
结论:
- 轻量级的深度学习模型,特别是MobileNetV3,为脑瘤分类提供了高度准确和计算高效的解决方案.
- 这些模型显示了现实世界临床部署的潜力.
- 优化的轻量级网络提供可解释和准确的诊断工具.
相关概念视频
Classification of Neurotransmitters
3.6K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
3.6K
Aggregates Classification
387
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
387
Seizures: Classification
605
Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
605
Classification of Systems-I
318
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
318
Classification of Systems-II
242
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
242
Classification of Signals
908
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
908


