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30 September 2024 Comparison of performance of two deep learning models for classification of skin lesions using image resampling technique for data augmentation
Al Mahmud, Hanieh Ajami, Md Sami Ul Hoque, Roshan Silwal, Mahdi Kargar Nigjeh, Scott E. Umbaugh
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Proceedings Volume 13137, Applications of Digital Image Processing XLVII; 1313705 (2024) https://doi.org/10.1117/12.3027916
Event: Optical Engineering + Applications, 2024, San Diego, California, United States
Abstract
This paper introduces an automated system comparing VGG16 and ResNet50 for dermatoscopic image processing and classification. This method utilized transfer learning and fine-tuning VGG16 and ResNet50 using the HAM10000 dataset. Random resampling balanced the dataset, optimizing models for accurate results with limited resources. We preprocessed images, performed data augmentation, modified the pre-existing models, and tuned the hyperparameters to increase the overall accuracy of both the models. Results demonstrate VGG16 and ResNet50 achieving 93.45% and 94.69% accuracy, respectively, showcasing the effectiveness of the proposed system in advancing early skin cancer intervention with deep learning techniques. Time comparison shows that VGG16 is 4 times faster than the other indicating the time-complexity of ResNet50 when skin lesion images are used for training.
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Al Mahmud, Hanieh Ajami, Md Sami Ul Hoque, Roshan Silwal, Mahdi Kargar Nigjeh, and Scott E. Umbaugh "Comparison of performance of two deep learning models for classification of skin lesions using image resampling technique for data augmentation", Proc. SPIE 13137, Applications of Digital Image Processing XLVII, 1313705 (30 September 2024); https://doi.org/10.1117/12.3027916
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KEYWORDS
Deep learning
Performance modeling
Skin
Image classification
Image processing
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