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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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Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique.

Chandran Kaushik Viknesh1, Palanisamy Nirmal Kumar1, Ramasamy Seetharaman1

  • 1Department of Electronics and Communication Engineering, College of Engineering Guindy Campus, Anna University, Chennai 600025, India.

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Summary

This study introduces computer-aided detection for early melanoma diagnosis using deep learning. Convolutional Neural Networks achieved 91% accuracy, outperforming Support Vector Machines, and are now in web and mobile apps.

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Djangoconvolutional neural networkmelanomaskin cancersupport vector machine

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Area of Science:

  • Dermatology
  • Computer Science
  • Artificial Intelligence

Background:

  • Human skin cancer, particularly melanoma, has a high mortality rate.
  • Early detection significantly improves treatment outcomes.
  • Traditional biopsy methods for melanoma diagnosis are invasive and time-consuming.

Purpose of the Study:

  • To develop and evaluate computer-aided detection (CAD) techniques for early melanoma diagnosis using image analysis.
  • To compare the performance of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) for skin cancer classification.
  • To deploy the most accurate model into accessible web and mobile applications.

Main Methods:

  • Two primary methods were investigated: CNNs (AlexNet, LeNet, VGG-16) and SVMs with RBF kernel.
  • Image processing techniques were applied to extract features for classification.
  • The CNN model with the highest accuracy was integrated into web and mobile applications using Django and Android Studio.

Main Results:

  • The CNN model achieved a classification accuracy of 91% after 100 epochs.
  • The SVM classifier demonstrated an accuracy of 86.6%.
  • The study explored the impact of model depth and dataset size on CNN performance.

Conclusions:

  • CNNs offer a highly accurate and efficient method for early melanoma detection compared to SVMs.
  • The developed CAD system, integrated into web and mobile platforms, enhances accessibility for early skin cancer diagnosis.
  • This research supports the advancement of AI in medical diagnostics for improved patient outcomes.