A method for skin lesion detection and localization by means of Deep Learning and reliable prediction explainability

IF 4.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Image and Vision Computing Pub Date : 2025-08-06 DOI:10.1016/j.imavis.2025.105675

Marcello Di Giammarco , Antonella Santone , Mario Cesarelli , Fabio Martinelli , Francesco Mercaldo

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Abstract

Skin lesions are any abnormal growths or appearances on the skin, ranging from benign (i.e., non-cancerous) to malignant (i.e., cancerous). The identification of a skin lesion is a crucial task that is carried out in short periods of time to initiate an eventual therapeutic treatment. In this paper, we propose a method for automatic skin lesion detection, implementing Convolutional Neural Networks. Moreover, with the aim of providing a rationale behind the model prediction, we also consider explainability by adopting two different Class Activation Mapping algorithms, which highlight regions in skin images that most contribute to the network’s classification decision. We also include the indices of similarity for further quantitative analysis. Several Convolutional Neural Networks are considered, by obtaining the best results with the MobileNet model, achieving an accuracy equal to 0.935 in skin lesion detection. Moreover, in the experimental analysis, we discuss the effectiveness of Class Activation Mapping algorithms exploited for skin lesion localization.

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一种基于深度学习和可靠预测可解释性的皮肤病变检测和定位方法

皮肤病变是皮肤上的任何异常生长或外观，从良性（即非癌性）到恶性（即癌性）不等。皮肤病变的识别是在短时间内进行最终治疗的关键任务。本文提出了一种基于卷积神经网络的皮肤损伤自动检测方法。此外，为了提供模型预测背后的基本原理，我们还通过采用两种不同的类激活映射算法来考虑可解释性，这些算法突出显示皮肤图像中最有助于网络分类决策的区域。我们还纳入了相似度指数，以便进一步定量分析。考虑了几种卷积神经网络，使用MobileNet模型得到了最好的结果，在皮肤病变检测中达到了0.935的准确率。此外，在实验分析中，我们讨论了类激活映射算法用于皮肤病变定位的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Image and Vision Computing 工程技术-工程：电子与电气

CiteScore

8.50

自引率

8.50%

发文量

143

审稿时长

7.8 months

期刊介绍： Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.