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In clinical applications, neural networks must focus on and highlight the most important parts of an input image. Soft-Attention mechanism enables a neural network to achieve this goal. This paper investigates the effectiveness of Soft-Attention in deep neural architectures. The central aim of Soft-Attention is to boost the value of important features and suppress the noise-inducing features. We com-pare the performance of VGG, ResNet, Inception ResNetv2 and DenseNet architectures with and without the Soft-Attention mechanism, while classifying skin lesions. The original network when coupled with Soft-Attention outperforms the baseline[15] by 4.7% while achieving a precision of 93.7% on HAM10000 dataset. Additionally, Soft-Attention coupling improves the sensitivity score by 3.8% compared to baseline[29] and achieves 91.6% on ISIC-2017 dataset. The code is publicly available at github

In clinical applications, neural networks must focus on and highlight the most important parts of an input image. Soft-Attention mechanism enables a neural network to achieve this goal. This paper investigates the effectiveness of Soft-Attention in deep neural architectures. The central aim of Soft-Attention is to boost the value of important features and suppress the noise-inducing features. We compare the performance of VGG, ResNet, InceptionResNetv2 and DenseNet architectures with and without the Soft-Attention mechanism, while classifying skin lesions. The original network when coupled with Soft-Attention outperforms the baseline[14] by 4.7% while achieving a precision of 93.7% on HAM10000 dataset. Additionally, Soft-Attention coupling improves the sensitivity score by 3.8% compared to baseline[28] and achieves 91.6% on ISIC-2017 dataset. The code is publicly available at github.

No abstract