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Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
Cerebral Hemispheres01:05

Cerebral Hemispheres

The human brain, a complex organ, is functionally divided into two cerebral hemispheres—left and right. These hemispheres are interconnected by a structure of paramount importance, the corpus callosum. This substantial bundle of neural fibers is not just a bridge between the hemispheres but a crucial element for the brain's comprehensive functioning. It enables efficient communication between the two hemispheres, allowing each side of the brain to control and receive sensory and motor...
Lateralization01:28

Lateralization

Brain lateralization refers to the division of mental processes and functions between the two hemispheres of the brain, a phenomenon that optimizes neural efficiency and underpins complex abilities in humans. This specialization allows each hemisphere to perform tasks where it has a comparative advantage, facilitating more refined cognitive capabilities across different domains.
Language and Cognition01:27

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
Learning Disabilities01:25

Learning Disabilities

Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...

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Related Experiment Video

Updated: Jun 18, 2026

Neuroimaging Field Methods Using Functional Near Infrared Spectroscopy (NIRS) Neuroimaging to Study Global Child Development: Rural Sub-Saharan Africa
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Neuroimaging Field Methods Using Functional Near Infrared Spectroscopy (NIRS) Neuroimaging to Study Global Child Development: Rural Sub-Saharan Africa

Published on: February 2, 2018

Grad-CAM Enhanced Explainable Deep Learning for Multi-Class Lung Cancer Classification Using DE-SAMNet Model.

Murat Kılıç1, Merve Bıyıklı1, Abdulkadir Yelman2

  • 1Turgut Ozal Medical Center, Department of Thoracic Surgery, Faculty of Medicine, Inonu University, Malatya 44280, Türkiye.

Diagnostics (Basel, Switzerland)
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces DE-SAMNet, a deep learning model for accurate lung cancer classification from CT scans. The explainable AI framework enhances diagnostic reliability and supports early detection.

Keywords:
classificationdensenet121efficientnetb0lung cancerspatial attention module

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Lung cancer (LC) is a leading cause of cancer mortality globally.
  • Manual interpretation of chest CT scans for LC diagnosis is time-consuming and variable.
  • Automated diagnostic tools are needed to improve accuracy and efficiency.

Purpose of the Study:

  • To develop and evaluate DE-SAMNet, a hybrid deep learning framework for automated multi-class lung cancer classification from CT scans.
  • To assess the model's performance on public and private clinical datasets.
  • To enhance the interpretability of the automated classification using explainable AI (XAI).

Main Methods:

  • DE-SAMNet integrates DenseNet121 and EfficientNetB0 for multi-scale feature extraction.
  • A Spatial Attention Module (SAM) refines feature representation by focusing on clinically relevant regions.
  • A compact fusion mechanism combines features for final classification.

Main Results:

  • The model achieved high performance on a public dataset (99.54% accuracy) and a private dataset (95.96% accuracy).
  • DE-SAMNet outperformed existing approaches in lung cancer classification.
  • XAI techniques (Grad-CAM) visualized decision-making, highlighting lesion-specific regions for transparency.

Conclusions:

  • DE-SAMNet provides a highly accurate and interpretable solution for automated lung cancer detection.
  • The explainability features enhance trust and demonstrate clinical potential for early diagnosis.
  • The framework addresses challenges in manual CT scan interpretation, improving diagnostic consistency.