  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 toachieve 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[16] by 4.7% while
achieving a precision of 93.7% on HAM10000 dataset [25]. Additionally,
Soft-Attention coupling improves the sensitivity score by 3.8% compared to
baseline[31] and achieves 91.6% on ISIC-2017 dataset [2]. The code is publicly
available at github.
