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Shahbaz Sikandar1, Rabbia Mahum1, Adham E Ragab2

  • 1Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan.

Diagnostics (Basel, Switzerland)
|June 10, 2023
PubMed
Summary
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A new convolutional neural network (CNN), SCDet, accurately detects tiny skin cancers. This method offers high precision and recall, improving early skin cancer diagnosis and patient survival rates.

Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Skin cancer, characterized by irregular lesions, poses a significant mortality risk in advanced stages.
  • Early detection of skin cancer is crucial for improving patient survival rates.
  • Existing diagnostic methods may struggle to identify the smallest tumors, necessitating improved detection techniques.

Purpose of the Study:

  • To propose a robust and accurate method for early skin cancer diagnosis using a novel deep learning model.
  • To develop a convolutional neural network (CNN) capable of detecting minute skin lesions that might be missed by other approaches.

Main Methods:

  • A 32-layer convolutional neural network (CNN) named SCDet was developed for skin lesion detection.
  • The model processes 227x227 images, utilizing pairs of convolution layers, batch normalization, and ReLU layers for pattern extraction.
Keywords:
batch normalizationbenignconvolution neural networkdermoscopic imagesmalignantmax poolingskin cancerskin lesionsoftmax

Related Experiment Videos

  • SCDet was trained and evaluated using key performance metrics including precision, recall, sensitivity, specificity, and accuracy.

    • Main Results:

    • SCDet achieved high performance metrics: 99.2% precision, 100% recall, 100% sensitivity, 99.20% specificity, and 99.6% accuracy.
    • The proposed SCDet model outperformed pre-trained models like VGG16, AlexNet, and SqueezeNet in accuracy and precision for detecting tiny skin tumors.
    • SCDet demonstrated superior speed and reduced computational cost compared to models such as ResNet50 due to its shallower architecture.

    Conclusions:

    • SCDet presents a highly effective and efficient deep learning solution for the early diagnosis of skin cancer.
    • The model's ability to detect the smallest lesions with high precision significantly enhances diagnostic capabilities.
    • SCDet offers a computationally cost-effective alternative for skin cancer detection, promising improved patient outcomes through early intervention.