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Updated: Jun 23, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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多模式脑瘤分类使用卷积网架构

M Padma Usha1, G Kannan1, M Ramamoorthy2

  • 1Department of Electronics and Communication Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, India.

Behavioural neurology
|June 17, 2024
PubMed
概括

这项研究介绍了Tumnet,这是一种深度学习方法,用于使用合并的MRI和CT图像进行脑瘤分类和细分. Tumnet实现了高精度,改善了对侵略性脑瘤的诊断和患者护理.

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Seizures: Classification

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:
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Classification of Systems-I

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:
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Classification of Systems-II01:31

Classification of Systems-II

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,
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Classification of Connective Tissues

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Connective Tissue Proper
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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.
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Aggregates Classification

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.
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