Soft-Attention Improves Skin Cancer Classification Performance

Soumyya Kanti Datta, Mohammad Abuzar Shaikh, Sargur N. Srihari, Mingchen Gao

2021-05-05Image Classification Skin Cancer Classification Lesion Classification General Classification Classification Cancer Classification

Paper PDF Code(official)

Abstract

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.

Related Papers

Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations2025-07-18 Adversarial attacks to image classification systems using evolutionary algorithms2025-07-17 Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy2025-07-17 Federated Learning for Commercial Image Sources2025-07-17 MUPAX: Multidimensional Problem Agnostic eXplainable AI2025-07-17 Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation2025-07-16 Safeguarding Federated Learning-based Road Condition Classification2025-07-16 Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking2025-07-15