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Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks
Nasser A AlSadhan1, Shatha Ali Alamri2, Mohamed Maher Ben Ismail1
1Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia.
Cancers
|April 13, 2024
Summary
This study shows YOLOv7 excels at identifying skin lesions, outperforming other models. This AI tool aids dermatologists in early skin cancer detection, potentially reducing unnecessary biopsies.
Area of Science:
- Dermatology
- Artificial Intelligence
- Medical Imaging
Background:
- Global rise in skin cancer incidence necessitates improved diagnostic tools.
- Distinguishing malignant melanoma from benign lesions is challenging due to visual similarities.
- Image-based recognition systems offer potential to assist dermatologists and reduce biopsies.
Purpose of the Study:
- To evaluate the performance of four unified convolutional neural networks (YOLOv3, YOLOv4, YOLOv5, YOLOv7) for skin lesion classification.
- To compare these models based on lesion localization, classification accuracy, and inference speed.
- To identify the most effective YOLO model for aiding in early skin cancer diagnosis.
Main Methods:
- Training four YOLO (You Only Look Once) models (v3, v4, v5, v7) on a benchmark skin lesion dataset.
- Evaluating model performance using metrics such as Intersection over Union (IoU), mean Average Precision (mAP), and F1-measure.
- Measuring the inference time for each model to assess real-time applicability.
Main Results:
- YOLOv7 demonstrated superior performance among the evaluated models.
- YOLOv7 achieved an IoU of 86.3%, mAP of 75.4%, and F1-measure of 80%.
- YOLOv7 exhibited an efficient inference time of 0.32 seconds per image.
Conclusions:
- YOLOv7 shows significant potential as an AI tool for dermatologists.
- The model can aid in the early and accurate diagnosis of skin cancer.
- Implementing YOLOv7 may help reduce the number of unnecessary biopsies performed.
