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Related Concept Videos

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,...
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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Related Experiment Video

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An efficient breast cancer classification model using bilateral filtering and fuzzy convolutional neural network.

A Abdul Hayum1, J Jaya2, R Sivakumar3

  • 1Electronics and Communication Engineering, Hindusthan Institute of Technology, Coimbatore, 641032, India. aabdulhayum@gmail.com.

Scientific Reports
|March 16, 2024
PubMed
Summary

This study introduces an improved breast cancer detection model using advanced image processing and machine learning techniques. The new method enhances accuracy and efficiency in classifying breast cancer subtypes, outperforming previous approaches.

Keywords:
Breast cancerComputer-aided detectionFuzzy convolutional neural networkModified fuzzy C means clusteringMutation chicken swarm optimization

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Artificial Intelligence

Background:

  • Breast cancer (BC) remains a leading cause of cancer mortality in women.
  • Previous BC classification models utilized median filters, Weighted K-Means Clustering (WKM), and Improved Cuckoo Search Optimization (ICSO) with Modified Recurrent Neural Networks (MRNN).
  • Existing methods faced challenges including slow search speeds, low convergence accuracy, and difficulties in MRNN training.

Purpose of the Study:

  • To develop a more accurate and efficient breast cancer detection and classification model.
  • To overcome the limitations of previous models, specifically slow optimization and complex training procedures.
  • To enhance image preprocessing, feature extraction, and classification stages for improved diagnostic performance.

Main Methods:

  • Image preprocessing using bilateral filtering and contrast stretching.
  • Region of Interest (ROI) segmentation via modified fuzzy C-means (MFCM) clustering.
  • Feature extraction using Center Distance Function (CDF)-based and Diagonal Texture Matrix (CDTM)-based methods, including color histograms and shape descriptors.
  • Dimensionality reduction with Kernel Principal Component Analysis (KPCA).
  • Feature selection using Mutational Chicken Flock Optimization (MCSO).
  • Classification using Fuzzy Convolutional Neural Network (FCNN) with MCSO-optimized parameters.

Main Results:

  • The proposed model achieved high accuracy, recall, and f-measure values.
  • Experimental results demonstrated superior performance compared to existing breast cancer classification models.
  • The optimized FCNN effectively detected and classified breast cancer subtypes.

Conclusions:

  • The enhanced model, incorporating bilateral filtering, MFCM, MCSO, and FCNN, offers a significant improvement in breast cancer detection and classification.
  • The study successfully addressed the limitations of prior methods, providing a more robust and accurate diagnostic tool.
  • The findings suggest a promising direction for AI-driven breast cancer diagnostics.