In this work, we employed transfer learning to build deep convolutional neural networks (CNNs) to distinguish measles rash from other skin conditions.

Sep 11, 2024 · On our curated image dataset, our model reaches a classification accuracy 94%, a sensitivity 74%, and specificity 97%. This measles rash image ...

May 18, 2020 · To assist in diagnosing measles, we collected more than 1300 images of a variety of skin conditions, with which we employed residual deep ...

This work employed transfer learning to build deep convolutional neural networks (CNNs) to distinguish measles rash from other skin conditions and will ...

They segmented skin lesion and sent segmented images to CNN for detection. Their model distinguishes between melanoma and benign cases with an accuracy of 81%.

To assist in diagnosing measles, we collected more than 1300 images of a variety of skin conditions, with which we employed residual deep convolutional neural ...

A dataset of more than 1300 images of a variety of skin conditions was created and a deep convolutional neural network was utilized to distinguish measles ...

They achieved a sensitivity of 81.7%, specificity of 97.1%, and accuracy of 95.2% using the ResNet-50 model [11] over the diverse rash image dataset. ... ...

On our curated image dataset, our model reaches a classification accuracy 95.2%, a sensitivity 81.7%, and specificity 97.1%. Our model can potentially be used ...

On our curated image dataset, our model reaches a classification accuracy 94 measles rash image detection could facilitate an accurate and early detection to ...