Abstract:

Skin disease detection using deep learning is an emerging field of research that aims to improve the accuracy and efficiency of skin disease diagnosis. Deep learning algorithms, particularly Convolutional Neural Networks (CNNs), have shown promising results in detecting various types of skin diseases from images. The process involves training a deep learning model using a large dataset of labelled skin disease images and then using the model to classify new images into their respective disease categories. Several research papers have proposed different deep learning-based approaches for skin disease detection using various techniques such as CNNs, GANs, and auto encoders. While the results of these studies are promising, more research is needed to further refine and optimize the algorithms and to ensure their generalizability and reliability in real-world clinical settings. Overall, skin disease detection using deep learning has the potential to revolutionize the field of dermatology and improve patient outcomes by providing an accurate and efficient diagnosis. Deep learning algorithms, particularly Convolutional Neural Networks (CNNs), have shown promising results in detecting various types of skin diseases from images. The process involves training a deep learning model using a large dataset of skin disease images labelled with their corresponding disease types. The trained model can then be used to classify new images into their respective disease categories.

Description:

CNN, SKIN DISEASE CLASSIFICATION, DEEP LEARNING, PYTHON