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Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
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Related Experiment Video

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Image Registration based Cervical Cancer Detection and Segmentation Using ANFIS Classifier

B Karthiga Jaya1, S Senthil Kumar

  • 1ECE, Dhanalakshmi Srinivasan Engineering College, Tamilnadu, India.

Asian Pacific Journal of Cancer Prevention : APJCP
|November 30, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an Adaptive Neuro Fuzzy Inference System (ANFIS) for cervical cancer detection and segmentation. The ANFIS method significantly improves accuracy, sensitivity, and specificity in identifying cancerous regions in cervical images.

Keywords:
Cervical cancerfeature extractionregistrationclassificationsegmentation

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Area of Science:

  • Medical Imaging
  • Computational Intelligence
  • Oncology

Background:

  • Cervical cancer remains a leading global health concern for women.
  • Accurate and early detection are crucial for effective treatment and improved patient outcomes.

Purpose of the Study:

  • To propose an Adaptive Neuro Fuzzy Inference System (ANFIS) based methodology for cervical cancer detection and segmentation.
  • To enhance the accuracy and reliability of cervical cancer diagnosis through automated image analysis.

Main Methods:

  • Image registration using Fast Fourier Transform (FFT).
  • Feature extraction including Grey Level Co-occurrence Matrix (GLCM), grey level, and trinary features.
  • Classification using the ANFIS classifier.
  • Segmentation of cancerous regions via morphological operations.

Main Results:

  • The proposed ANFIS-based methodology demonstrated superior performance compared to existing methods.
  • Significant improvements were observed in sensitivity, specificity, and overall accuracy for cervical cancer detection and segmentation.
  • Simulations on a large dataset validated the effectiveness of the approach.

Conclusions:

  • The ANFIS classifier provides a robust framework for cervical cancer detection and segmentation.
  • This automated approach holds promise for improving diagnostic capabilities in cervical cancer screening.
  • The methodology offers a significant advancement in the field of medical image analysis for oncology.