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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Updated: May 30, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Optimizing Skin Cancer Diagnosis: A Modified Ensemble Convolutional Neural Network for Classification.

A M Vidhyalakshmi1, M Kanchana1

  • 1Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India.

Microscopy Research and Technique
|January 31, 2025
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Summary

This study introduces a novel Random Cat Swarm Optimization with Ensemble Convolutional Neural Network (RCS-ECNN) for accurate skin cancer detection. The RCS-ECNN method significantly improves early skin cancer diagnosis and classification accuracy.

Keywords:
GrabCut algorithmKeras deep neural networkcat swarm optimizationdeep neural networkskin cancer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Skin cancer is a major global health concern, necessitating improved diagnostic methods.
  • Traditional skin cancer detection techniques suffer from scalability and overfitting limitations.
  • Early detection is crucial for effective skin cancer treatment outcomes.

Purpose of the Study:

  • To propose a novel Random Cat Swarm Optimization with Ensemble Convolutional Neural Network (RCS-ECNN) for skin cancer stage classification.
  • To enhance the accuracy and efficiency of skin cancer detection using deep learning.
  • To address the limitations of existing skin cancer diagnostic approaches.

Main Methods:

  • Utilized two deep learning classifiers: Deep Neural Network (DNN) and Keras DNN (KDNN).
  • Implemented an effective preprocessing phase, feature extraction, and GrabCut algorithm for segmentation.
  • Employed Random Cat Swarm Optimization (CSO) to optimize the ensemble convolutional neural network (ECNN) model.
  • Evaluated the RCS-ECNN method on the HAM10000 and ISIC datasets.

Main Results:

  • The RCS-ECNN method achieved high performance metrics: 99.56% accuracy, 99.66% recall, 99.254% specificity, 99.18% precision, and 98.545% F1-score.
  • Demonstrated superior performance compared to existing skin cancer detection techniques.
  • The proposed method effectively categorizes different stages of skin cancer.

Conclusions:

  • The RCS-ECNN method presents a highly accurate and efficient approach for skin cancer detection and classification.
  • This deep learning-based strategy overcomes the limitations of traditional methods.
  • The findings suggest significant potential for improving early skin cancer diagnosis in clinical settings.