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Related Experiment Video

Updated: Jul 7, 2026

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

ZACP: enhancing skin lesion classification ability using zero attention and complete perception.

Hailong Zuo1, Zhi Weng2, Zhenshuai Fu1

  • 1College of Electronic Information Engineering, Inner Mongolia University, Hohhot, Inner Mongolia, 010021, China.

Visual Computing for Industry, Biomedicine, and Art
|July 6, 2026
PubMed
Summary

Related Concept Videos

Skin Cancer01:30

Skin Cancer

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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This study introduces the Zero Attention and Complete Perception (ZACP) system for accurate skin lesion classification. ZACP enhances diagnostic capabilities, improving patient care by focusing on critical lesion features.

Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Increasing dermatology patient load strains hospital diagnostics.
  • Delayed diagnosis and poor patient experience necessitate improved classification tools.
  • Ecological deterioration and lifestyle changes contribute to rising skin conditions.

Purpose of the Study:

  • To develop an accurate skin lesion classification system.
  • To address diagnostic pressure in dermatology departments.
  • To enhance the focus on relevant lesion features for improved accuracy.

Main Methods:

  • Proposed the Zero Attention and Complete Perception (ZACP) system, built upon EfficientNet.
  • Integrated Zero Attention (ZA) for focused lesion analysis and Complete Perception (CP) for multiscale feature capture.
Keywords:
Complete perceptionEfficientNetSkin lesionVision transformerZero attention mechanism

Related Experiment Videos

Last Updated: Jul 7, 2026

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

  • Utilized patch segmentation, positional encoding, Mish activation, and evaluated on HAM10000 and ISIC2017 datasets.

    • Main Results:

    • ZACP achieved 95% accuracy on the HAM10000 dataset.
    • ZACP achieved 91% accuracy on the ISIC2017 dataset.
    • Outperformed comparative models in accuracy and other key metrics.

    Conclusions:

    • The ZACP system demonstrates superior performance in skin lesion classification.
    • This novel approach can alleviate diagnostic pressure and improve patient outcomes.
    • The developed system offers a promising tool for automated dermatological diagnostics.