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Related Experiment Video

Updated: May 5, 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

2.8K

Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila

Jalaleddin Mohamed1, Necmi Serkan Tezel1, Javad Rahebi2

  • 1Electrical and Electronics Engineering Department, Karabuk University, 78050 Karabuk, Türkiye.

Diagnostics (Basel, Switzerland)
|March 28, 2025
PubMed
Summary

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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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This study introduces a novel deep learning and Aquila Optimizer (DL-AO) framework for enhanced melanoma detection. The DL-AO system significantly improves accuracy and efficiency in identifying skin cancer, outperforming existing methods.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Melanoma is an aggressive skin cancer requiring early detection.
  • Current detection methods face challenges in accuracy and efficiency.
  • Novel computational approaches are needed to improve melanoma diagnosis.

Purpose of the Study:

  • To develop a new classification system for melanoma detection.
  • To integrate Convolutional Neural Networks (CNNs) with the Aquila Optimizer (AO).
  • To enhance computational efficiency and diagnostic accuracy for melanoma.

Main Methods:

  • Utilized CNNs for feature extraction from melanoma images.
  • Employed the Aquila Optimizer (AO) for feature dimension reduction.
  • Evaluated the hybrid approach on ISIC 2019, ISBI 2016, and ISBI 2017 datasets.
Keywords:
Aquila Optimizerconvolutional neural networkfeature dimensions reductionmelanoma skin cancer

Related Experiment Videos

Last Updated: May 5, 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

2.8K

Main Results:

  • Achieved high performance across datasets, including up to 98.89% specificity and 98.42% accuracy.
  • Demonstrated superior performance over existing techniques with significant improvements in accuracy, sensitivity, and specificity.
  • Reported a reduction in computational complexity by up to 37.5% due to AO.

Conclusions:

  • The deep learning-Aquila Optimizer (DL-AO) framework provides an efficient and accurate method for melanoma detection.
  • The approach is suitable for resource-constrained environments like mobile and edge computing.
  • Integrating deep learning with metaheuristic optimization enhances melanoma detection accuracy, robustness, and efficiency.