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Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

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Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

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Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
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Cardiovascular Drugs: Classification based on Therapeutic Indications01:18

Cardiovascular Drugs: Classification based on Therapeutic Indications

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Cardiovascular diseases, encompassing a range of conditions, can significantly affect the heart's operations and the overall circulatory system. These conditions impair the heart's ability to pump blood, leading to a deficit in oxygen supply to crucial organs. Anomalies in the heart's electrical system, known as arrhythmias, can cause heartbeats to accelerate or slow down. Usually, heart rates increase during physical activity and decrease while resting or sleeping. However,...
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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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Classification of Bones01:18

Classification of Bones

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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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IA explicable impulsada por la clasificación de tumores cerebrales basada en RM: un enfoque novedoso de aprendizaje

Vinayaka R Srinivas1, Ramasubramanian Parvathi1

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.

Frontiers in artificial intelligence
|January 26, 2026
PubMed
Resumen

Un marco novedoso de aprendizaje profundo clasifica eficientemente los tumores cerebrales a partir de imágenes de RM con una precisión del 95,86 %. Este enfoque muestra una gran promesa para mejorar las herramientas de diagnóstico en entornos clínicos.

Palabras clave:
RMclasificación de tumores cerebralesredes neuronales convolucionalesaumento de datosaprendizaje profundoIA explicablevisualización de característicasimagen médica

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Área de la Ciencia:

  • Imagen Médica
  • Inteligencia Artificial
  • Oncología

Sus antecedentes:

  • Los tumores cerebrales representan un desafío oncológico significativo, que requiere métodos de diagnóstico precisos.
  • Los procesos de diagnóstico actuales requieren mejoras para obtener mejores resultados para los pacientes.

Objetivo del estudio:

  • Desarrollar un marco de aprendizaje profundo eficiente para la clasificación de tumores cerebrales utilizando datos de RM.
  • Lograr una alta precisión en la diferenciación entre tejido normal y varios tipos de tumores cerebrales (glioma, pituitario, meningioma).

Principales métodos:

  • Se utilizaron redes neuronales convolucionales (CNN), incluidas las arquitecturas DenseNet50 y VGG19.
  • Se aplicaron técnicas de preprocesamiento como reducción de ruido, cambio de tamaño y aumento de datos.
  • Se emplearon métodos de IA explicable (XAI) como Grad-CAM y LIME para la interpretabilidad del modelo.

Principales resultados:

  • Un modelo CNN de 4-conv-1-dense-1-dropout logró una precisión de clasificación del 95,86 %.
  • El modelo CNN desarrollado superó a las arquitecturas más profundas y a los modelos de aprendizaje por transferencia.
  • Las técnicas de XAI proporcionaron información sobre el proceso de toma de decisiones del modelo.

Conclusiones:

  • Los modelos de aprendizaje profundo ofrecen una solución confiable y eficiente para la clasificación de tumores cerebrales.
  • El estudio recomienda la implementación clínica en tiempo real y la futura integración con modelos de lenguaje grandes (LLM) para la generación automática de informes.