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Drug Classes and Categories01:25

Drug Classes and Categories

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Drugs can be classified according to their chemical composition or their intended therapeutic application. For instance, anti-infective agents that possess the ability to eliminate pathogens or suppress their growth and reproduction can be grouped based on the organisms they target or their chemical structure. Furthermore, drugs can be divided into prescription, nonprescription, or controlled substances. Prescription medications, such as antibiotics, require oversight from a licensed healthcare...
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Antibody Structure and Classes01:25

Antibody Structure and Classes

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Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
The basic structure of an antibody consists of four protein chains: two identical heavy chains and two identical light chains. These chains are held together by disulfide bonds and other non-covalent interactions, forming a Y-shaped structure.
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Wave Parameters01:10

Wave Parameters

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The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Classification of Neurotransmitters01:30

Classification of Neurotransmitters

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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...
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Classification of Leukocytes01:30

Classification of Leukocytes

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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
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Related Experiment Video

Updated: Feb 10, 2026

Author Spotlight: Implications of Non-Nutritive Sucking on Speech Emergence and Infant Development
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CerevianNet: parameter efficient multi-class brain tumor classification using custom lightweight CNN.

Md Khurshid Jahan1, Abdullah Al Shafi1, Maher Ali Rusho2

  • 1Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.

Frontiers in Medicine
|February 9, 2026
PubMed
Summary

This study introduces a lightweight custom convolutional neural network (CNN) for scalable brain tumor classification on small devices. The novel framework achieves high accuracy, offering a faster, more efficient alternative to traditional methods for early brain tumor detection.

Keywords:
MRIbrain tumorcustom lightweight CNNlight weightmedical imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Traditional manual brain tumor diagnosis is time-consuming and prone to errors.
  • Computer-Aided Diagnostic (CAD) systems offer faster, scalable solutions.
  • Deep learning models face challenges like overfitting with limited data.

Purpose of the Study:

  • To propose a scalable multi-class brain tumor classification framework for small-form-factor devices.
  • To develop a lightweight custom convolutional neural network (CNN) for efficient brain tumor diagnosis.
  • To evaluate the performance of the custom CNN against state-of-the-art deep learning models.

Main Methods:

  • Developed a novel, lightweight custom convolutional neural network (CNN).
  • Evaluated the custom CNN and pretrained models (EfficientNetb3, ResNet, etc.) on five diverse brain tumor datasets.
  • Optimized the framework for small-form-factor devices and assessed performance on varying dataset sizes and balances.

Main Results:

  • The custom lightweight CNN achieved 98% accuracy with significantly fewer parameters and reduced training time compared to other models.
  • EfficientNetb3 demonstrated the highest accuracy at 99.11%.
  • The model performed well on larger datasets but struggled with smaller, imbalanced ones, highlighting data dependency.

Conclusions:

  • The proposed framework effectively utilizes deep learning for accurate brain tumor classification, approaching expert performance.
  • The lightweight custom CNN offers an efficient and scalable solution suitable for clinical integration.
  • This research facilitates the deployment of AI in medical applications for improved brain tumor diagnosis accessibility.