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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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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.
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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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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.
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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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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.
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CerevianNet: clasificación de tumores cerebrales de varias clases eficiente por parámetros utilizando CNN ligero

Md Khurshid Jahan1, Abdullah Al Shafi1, Maher Ali Rusho2

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

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Resumen

Este estudio introduce una red neuronal convolucional personalizada ligera (CNN) para la clasificación escalable de tumores cerebrales en dispositivos pequeños. El nuevo marco alcanza una alta precisión, ofreciendo una alternativa más rápida y eficiente a los métodos tradicionales para la detección temprana de tumores cerebrales.

Palabras clave:
La resonancia magnética es una resonancia magnética.El tumor cerebral es un tumor cerebral.personalizado ligero CNN CNN ligero personalizado.peso ligero peso ligero.imágenes médicas de imagen.

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Área de la Ciencia:

  • Imágenes médicas de imágenes médicas.
  • La inteligencia artificial es inteligencia artificial.
  • Biología computacional Biología computacional.

Sus antecedentes:

  • El diagnóstico manual tradicional de tumores cerebrales requiere mucho tiempo y es propenso a errores.
  • Los sistemas de diagnóstico asistido por computadora (CAD) ofrecen soluciones más rápidas y escalables.
  • Los modelos de aprendizaje profundo enfrentan desafíos como el exceso de ajuste con datos limitados.

Objetivo del estudio:

  • Proponer un marco escalable de clasificación de tumores cerebrales de varias clases para dispositivos de factor de forma pequeño.
  • Desarrollar una red neuronal convolucional personalizada ligera (CNN) para el diagnóstico eficiente de tumores cerebrales.
  • Para evaluar el rendimiento de la CNN personalizada frente a los modelos de aprendizaje profundo de última generación.

Principales métodos:

  • Desarrolló una nueva y ligera red neuronal convolucional personalizada (CNN).
  • Evaluó la CNN personalizada y los modelos preentrenados (EfficientNetb3, ResNet, etc.) en cinco diferentes conjuntos de datos de tumores cerebrales.
  • Se optimizó el marco para dispositivos con factor de forma pequeño y se evaluó el rendimiento en diferentes tamaños y balances de conjuntos de datos.

Principales resultados:

  • El CNN ligero personalizado logró una precisión del 98% con significativamente menos parámetros y un tiempo de entrenamiento reducido en comparación con otros modelos.
  • EfficientNetb3 demostró la mayor precisión con un 99,11%.
  • El modelo funcionó bien en conjuntos de datos más grandes, pero tuvo problemas con los más pequeños y desequilibrados, destacando la dependencia de los datos.

Conclusiones:

  • El marco propuesto utiliza efectivamente el aprendizaje profundo para una clasificación precisa de tumores cerebrales, acercándose al rendimiento de los expertos.
  • La CNN personalizada ligera ofrece una solución eficiente y escalable adecuada para la integración clínica.
  • Esta investigación facilita el despliegue de la IA en aplicaciones médicas para mejorar la accesibilidad al diagnóstico de tumores cerebrales.