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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Deep Learning-Based Skin Lesion Multi-class Classification with Global Average Pooling Improvement.

Paravatham V S P Raghavendra1, C Charitha2, K Ghousiya Begum3

  • 1School of Mechanical Engineering, SASTRA Deemed to be University, 613401, Thanjavur, India.

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|July 5, 2023
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Summary
This summary is machine-generated.

A new deep convolutional neural network (DCNN) model accurately identifies and classifies skin lesions. This AI tool achieved 97.20% accuracy, outperforming existing methods for early skin cancer detection.

Keywords:
Deep Convolution Neural Network (DCNN)Deep learningGUIHAM10000PredictionSkin cancer

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Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Skin cancer diagnosis relies on visual inspection and biopsy, which have limitations in accuracy and accessibility.
  • Developing automated systems for skin lesion classification is crucial for early detection and improved patient outcomes.

Purpose of the Study:

  • To propose a novel deep convolutional neural network (DCNN) model for accurate multi-class skin lesion identification and classification.
  • To enhance the performance of skin cancer detection using advanced deep learning techniques.

Main Methods:

  • A novel DCNN model incorporating global average pooling was developed for skin lesion analysis.
  • The HAM10000 dataset, comprising seven skin lesion classes, was utilized for model training and validation.
  • Preprocessing involved black hat filtering for artifact removal and resampling for data balancing.

Main Results:

  • The proposed DCNN model achieved a highest accuracy of 97.20% in multi-class skin lesion classification.
  • Performance was benchmarked against established transfer learning models like ResNet50, VGG-16, MobileNetV2, and DenseNet121.
  • Model efficacy was visually confirmed using a graphical user interface (GUI).

Conclusions:

  • The developed DCNN model demonstrates superior performance for automated skin lesion classification compared to existing state-of-the-art methods.
  • This AI-driven tool shows significant potential as a computer-aided diagnostic aid for dermatologists.
  • The findings support the advancement of deep learning applications in medical diagnostics for improved healthcare.