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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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Reducing Line Loss01:18

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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An improved transformer network for skin cancer classification.

Chao Xin1, Zhifang Liu1, Keyu Zhao1

  • 1The Affiliated Hospital of Medical School, Ningbo University, Ningbo, 315020, China.

Computers in Biology and Medicine
|August 29, 2022
PubMed
Summary

Artificial intelligence, specifically the SkinTrans vision transformer, shows promise for early skin cancer detection. This AI model achieved high accuracy in classifying dermoscopic images, aiding in timely diagnosis and treatment.

Keywords:
ClassificationContrastive learningSkin cancerVision transformer

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Area of Science:

  • Artificial Intelligence in Dermatology
  • Medical Image Analysis
  • Computer Vision for Healthcare

Background:

  • Global rise in skin cancer incidence necessitates improved early detection methods.
  • Convolutional Neural Networks (CNNs) have advanced skin cancer image classification but struggle with feature localization.
  • Limitations of CNNs in extracting critical localized features for accurate diagnosis.

Purpose of the Study:

  • To introduce SkinTrans, an improved vision transformer (VIT) network for enhanced skin cancer classification.
  • To leverage self-attention mechanisms for prioritizing significant features and reducing noise in dermoscopic images.
  • To improve the accuracy and localization capabilities in AI-driven skin cancer diagnosis.

Main Methods:

  • Development and validation of the SkinTrans model based on the vision transformer architecture.
  • Implementation of multi-scale patch embedding using sliding windows to capture diverse image features.
  • Application of contrastive learning to ensure distinct encoding of dissimilar skin cancer data.

Main Results:

  • SkinTrans achieved 94.3% accuracy on the HAM10000 dataset.
  • The model demonstrated 94.1% accuracy on a clinical dermoscopy dataset.
  • Experimental results confirm the high efficiency and effectiveness of the proposed SkinTrans model.

Conclusions:

  • Vision transformers show significant potential for medical image analysis, extending beyond natural language processing.
  • SkinTrans provides a robust foundation for multimodal data-driven skin cancer classification.
  • The findings are relevant for dermatologists, researchers, and computer scientists, potentially improving patient care.