Multi-Class Skin Diseases Classification Using Hybrid Deep Convolutional Neural Network
Keywords:
Deep Convolution Neural Network, Skin Diseases, Long Short-Term Memory, Image processing, Multi-class diseasesAbstract
There are many obstacles to accurate skin disease diagnosis and quick treatment. In this paper, we provide a multi-class skin disease categorization framework that incorporates a powerful convolutional neural network (CNN), MobileNet V2 (MNV2), and a LSTM (Long Short Term Memory) in order to increase the accuracy and dependability of diagnosis. The suggested approach makes use of LSTM’s capacity to handle multi-class classification tasks and CNN's ability to automatically learn discriminative features from raw skin images. Multiple convolutional layers are followed by fully connected layers in the proposed hybrid CNN architecture. To improve gradient flow and lessen the vanishing gradient issue, it contains residual connections. Additionally, it integrates attention techniques to deliberately highlight informative areas in the skin images, improving the network's ability to discriminate. The suggested solution outperforms previous methods with an accuracy greater than 87% and makes use of the HAM10000 dataset to do so. Additionally, it displays exceptional stability in quickly locating the damaged area while using almost half as much computational resources as the traditional MobileNet model. This results in substantially less computational work being required without sacrificing classification speed or accuracy.
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