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Skin Cancer01:30

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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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Skin Lesion Classification Using Focal Modulation Networks.

Hasan Zan1

  • 1Department of Computer Engineering, Mardin Artuklu University, Mardin, Turkey.

Annals of the New York Academy of Sciences
|November 3, 2025
PubMed
Summary
This summary is machine-generated.

Focal Modulation Networks (FMNs) offer an accurate and efficient method for classifying skin lesions from dermoscopic images. This approach enhances early skin cancer diagnosis with improved interpretability and performance on public datasets.

Keywords:
deep learningfocal modulation networksskin cancer detectionskin lesion classification

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Automated skin lesion classification is vital for early skin cancer detection.
  • Challenges include visual similarity, lesion variability, and image artifacts.
  • Current deep learning models are often computationally intensive and lack interpretability.

Purpose of the Study:

  • To propose and evaluate a framework using Focal Modulation Networks (FMNs) for skin lesion classification.
  • To address limitations of existing models in processing high-resolution medical images and clinical integration.

Main Methods:

  • Developed a framework utilizing four variants of Focal Modulation Networks (FMNs): Tiny, Small, Base, and Large.
  • Evaluated the FMN framework on three public datasets: ISIC 2017, ISIC 2018, and ISIC 2019.
  • Assessed classification accuracy, computational efficiency, and model interpretability.

Main Results:

  • Achieved high classification accuracies: 97.8% on ISIC 2019, 96.4% on ISIC 2018, and 88.1% on ISIC 2017.
  • Results meet or surpass those of previous studies.
  • Demonstrated model interpretability via modulator visualization.

Conclusions:

  • FMNs provide an accurate, efficient, and transparent solution for automated skin lesion classification.
  • The proposed method is suitable for clinical integration due to its performance and interpretability.
  • FMNs effectively capture local and global features, outperforming transformers on high-resolution medical images.