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Related Concept Videos

Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

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Advanced dynamic ensemble framework with explainability driven insights for precision brain tumor classification

Retinderdeep Singh1, Sheifali Gupta1, Ashraf Osman Ibrahim2,3

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.

Scientific Reports
|August 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced deep learning system for precise brain tumor classification from MRI scans. Achieving 99.4% accuracy, it enhances diagnostic reliability and interpretability in medical imaging.

Keywords:
Brain tumorDynamic weightsEfficientNetEnsemble modelExplainable AIResNet

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning

Background:

  • Accurate brain tumor detection is challenging due to tumor diversity and human error.
  • Existing diagnostic methods require improvement for precision and reliability.

Purpose of the Study:

  • To develop a novel ensemble deep learning system for accurate brain tumor classification using MRI data.
  • To enhance diagnostic accuracy and provide interpretability in brain tumor detection.

Main Methods:

  • An ensemble framework integrating fine-tuned Convolutional Neural Network (CNN), ResNet-50, and EfficientNet-B5.
  • Adaptive dynamic weight distribution and a customized weighted cross-entropy loss function.
  • Explainable AI (XAI) techniques (Grad-CAM, SHAP, SmoothGrad, LIME) and entropy-based uncertainty analysis.

Main Results:

  • Achieved high classification accuracy: 99.4% (test), 99.48% (validation), and 99.31% (cross-dataset).
  • Demonstrated robust interpretability through XAI techniques.
  • Quantified prediction confidence with an average entropy of 0.3093, identifying uncertain predictions.

Conclusions:

  • The proposed deep learning framework offers high accuracy, robustness, and interpretability for brain tumor classification.
  • It shows significant potential for integration into automated diagnostic systems, reducing errors.
  • The system addresses class imbalance and improves model generalization effectively.