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Related Concept Videos

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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.
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Related Experiment Video

Updated: Aug 15, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Squeeze-MNet: Precise Skin Cancer Detection Model for Low Computing IoT Devices Using Transfer Learning.

Rupali Kiran Shinde1, Md Shahinur Alam2, Md Biddut Hossain1

  • 1Department of Information and Communication Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.

Cancers
|January 8, 2023
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Summary
This summary is machine-generated.

A new deep learning algorithm, Squeeze-MNet, accurately classifies skin cancer using digital hair removal and a lightweight model. This approach enhances diagnostic accuracy and reduces dataset requirements for effective skin cancer detection.

Keywords:
AUC-ROCIoTMobileNetdeep learningmalignantskin cancer detectionsqueezed datasettransfer learning

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Dermatology

Background:

  • Skin cancer poses a significant global health challenge.
  • Accurate and accessible diagnostic tools are crucial for early detection and treatment.
  • Deep learning models show promise for automated medical image analysis.

Purpose of the Study:

  • To develop a lightweight and accurate deep learning algorithm for skin cancer classification.
  • To improve the efficiency of skin cancer detection using image preprocessing techniques.
  • To validate the performance of the developed model on a portable device.

Main Methods:

  • Developed Squeeze-MNet, integrating a Squeeze algorithm for digital hair removal and a pre-trained MobileNet model.
  • Employed a black-hat filter for noise reduction during image preprocessing.
  • Fine-tuned the MobileNet model using the International Skin Imaging Collaboration (ISIC) dataset.
  • Tested the lightweight prototype on a Raspberry Pi 4 Internet of Things device.

Main Results:

  • Achieved high average precision (AP) for benign (99.76%) and malignant (98.02%) diagnoses.
  • Demonstrated a 66% reduction in required dataset size.
  • Increased skin cancer detection accuracy to 99.36% with the ISIC dataset.
  • Obtained an area under the receiver operating curve (AUC) of 98.9%.

Conclusions:

  • Squeeze-MNet offers a lightweight, accurate, and general-purpose solution for skin cancer classification.
  • The integrated hair removal algorithm significantly improves diagnostic accuracy.
  • The model's performance on a portable device suggests potential for widespread clinical application.