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Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel Deep-Learning-Based Convolutional Neural Network
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DOI：10.3390/diagnostics12092048.png)
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摘要
En 中文
Skin is the primary protective layer of the internal organs of the body. Nowadays, due to increasing pollution and multiple other factors, various types of skin diseases are growing globally. With variable shapes and multiple types, the classification of skin lesions is a challenging task. Motivated by this spreading deformity in society, a lightweight and efficient model is proposed for the highly accurate classification of skin lesions. Dynamic-sized kernels are used in layers to obtain the best results, resulting in very few trainable parameters. Further, both ReLU and leakyReLU activation functions are purposefully used in the proposed model. The model accurately classified all of the classes of the HAM10000 dataset. The model achieved an overall accuracy of 97.85%, which is much better than multiple state-of-the-art heavy models. Further, our work is compared with some popular state-of-the-art and recent existing models.
Keyword:
deep learning
skin diseases
biomedical image
artificial intelligence
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期刊
IF:
3.3
论文数:
2.0W
被引数:
3.6W
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引用论文
Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM使用MobileNet V2和LSTM的深度学习神经网络对皮肤病进行分类.png)
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