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Related Experiment Video

Updated: Jan 29, 2026

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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DL-PCMNet: Distributed Learning Enabled Parallel Convolutional Memory Network for Skin Cancer Classification with

Afnan M Alhassan1, Nouf I Altmami1

  • 1Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia.

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|January 28, 2026
PubMed
Summary
This summary is machine-generated.

A new Distributed Learning enabled Parallel Convolutional Memory Network (DL-PCMNet) model accurately classifies skin cancer using deep learning. This method overcomes limitations in existing skin lesion classification techniques, improving diagnostic accuracy.

Keywords:
deep learningdermatoscopic imagedistributed learningmedical imagingskin cancer classification

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Area of Science:

  • Dermatology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Computational Pathology

Background:

  • Skin cancer is a rapidly spreading, lethal illness characterized by abnormal skin cell growth.
  • Classifying skin lesions and diagnosing tumors from dermoscopic images presents significant challenges.
  • Existing diagnostic methods suffer from insufficient data, computational complexity, class imbalance, and poor performance.

Purpose of the Study:

  • To introduce an advanced model for effective skin cancer classification.
  • To address limitations of current methods in accuracy and reliability.
  • To improve the diagnosis of skin lesions using deep learning.

Main Methods:

  • Development of the Distributed Learning enabled Parallel Convolutional Memory Network (DL-PCMNet) model.
  • Integration of distributed learning for enhanced flexibility and reliability.
  • Combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for robust feature extraction and dependency capture.
  • Application of advanced preprocessing and feature extraction techniques.

Main Results:

  • The DL-PCMNet model achieved high performance on the ISIC 2019 dataset.
  • Achieved 97.28% accuracy, 97.30% precision, 97.17% sensitivity, and 97.72% specificity at 90% training.
  • Demonstrated superior performance compared to existing skin cancer classification models.

Conclusions:

  • The proposed DL-PCMNet model offers an efficient and accurate solution for skin cancer classification.
  • This deep learning approach effectively overcomes previous diagnostic challenges.
  • The model shows significant potential for improving dermatological diagnostics.