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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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Modified Le-Net Model with Multiple Image Features for Skin Cancer Detection.

Vinay Kumar Y B1, Vimala H S1, Shreyas J2

  • 1Department of Computer Science and Engineering, University of Visvesvaraya College of Engineering (UVCE, IIT Model College) Bangalore University, Bengaluru, India.

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|June 19, 2025
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Summary

A new Modified LeNet (MLeNet) model with Improved DeepJoint Segmentation (IDJS) significantly enhances skin cancer detection accuracy. This deep learning approach achieves a 0.952 positive metric value, outperforming traditional methods for improved diagnostic solutions.

Keywords:
DermoscopyImproved DeepJoint segmentationImproved pyramid histogram of oriented gradient and Modified LeNetSkin cancer

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Area of Science:

  • Dermatology
  • Computer Science
  • Medical Imaging

Background:

  • Computer-based technologies offer non-invasive, cost-effective, and rapid solutions for skin cancer detection.
  • Accurate segmentation of cancerous regions is crucial for precise diagnosis.

Purpose of the Study:

  • To propose a novel Deep Learning (DL)-based approach for enhanced skin cancer detection.
  • To leverage an advanced segmentation technique, Improved DeepJoint Segmentation (IDJS), for improved accuracy.

Main Methods:

  • A Modified LeNet (MLeNet) model incorporating a Gaussian filter for noise reduction during preprocessing.
  • Application of IDJS for accurate segmentation of cancerous skin regions.
  • Extraction of Multi-Texton Histogram (MTH), Improved Pyramid Histogram of Oriented Gradient (IPHOG), and Median Binary Pattern (MBP) features for classification.

Main Results:

  • The MLeNet model achieved a positive metric value of 0.952 on the HAM10000 and ISIC 2019 datasets.
  • The proposed model demonstrated superior performance compared to traditional models, including LeNet (0.932).

Conclusions:

  • The developed DL-based approach significantly improves the accuracy and precision of skin cancer detection.
  • The MLeNet model, combined with IDJS and advanced feature extraction, presents a promising tool for dermatological diagnostics.