Arshad Mehak, Khan Muhammad Attique, Górriz Juan Manuel, Baili Jamel, AlHammadi Dina Abdulaziz, Kim Chomyong, Nam Yunyoung
Department of Computer Science, HITEC University, Taxila 47080, Pakistan.
Center of AI, Prince Mohammad bin Fahd University, Alkhobar, Saudi Arabia.
J Adv Res. 2025 Aug 26. doi: 10.1016/j.jare.2025.08.039.
Skin lesion segmentation and classification is an active research area in medical imaging for the large number of reported deaths in the recent years. Early diagnosis of skin cancer is essential to decrease the death rate and increase life expectancy.
Several artificial intelligence (AI) based techniques have been introduced in the literature for the diagnosing skin cancer; however, due to challenge of imbalanced datasets, irregular lesion shape, presence of lesions on boundary regions, and selection of inappropriate model selection, the performance of AI model is highly impacted. Therefore, in this work our main objective is to propose a fully automated deep framework for skin lesion segmentation and classification with more efficient and effective way.
This work proposes a novel framework for segmenting and classifying skin lesions using improved ResNet20-DeepLabV3+ and MAKNet100 deep models. In the segmentation task, a ResNet20 architecture is designed as a backbone of DeepLabV3+. In the designed ResNet20 architecture, a few grouped convolutional layers are added with smaller filter sizes to extract more insight information. A new MAKNet100 model is proposed in the classification task based on the network-level fusion of two custom models. The proposed network has few parameters and can extract more information about the lesion images. The proposed model is trained and further analyzed using the GradCAM explainable artificial technique (XAI) as a black box interpretation. Features are extracted from the self-attention layer and passed to classifiers for the final classification.
The experimental process of the proposed framework is performed on HAM10000, ISIC-2018, ISIC-2019, and ISBI-2020 datasets with an accuracy of 90.5 %, 88.9 %, 84.5 %, and 96.35 % respectively and the highest obtained dice score on ISIC-2018 and HAM10000 is 94.63 and 96.69 % respectively.
The proposed framework obtained improved accuracy and precision rates for skin lesion segmentation and classification on these datasets. Moreover, the ablation study and comparison with existing techniques show the proposed framework's dominance.
近年来，由于大量报告的死亡病例，皮肤病变分割和分类成为医学成像领域的一个活跃研究领域。皮肤癌的早期诊断对于降低死亡率和提高预期寿命至关重要。
文献中已经介绍了几种基于人工智能（AI）的技术来诊断皮肤癌；然而，由于数据集不平衡、病变形状不规则、边界区域存在病变以及选择不合适的模型等挑战，人工智能模型的性能受到很大影响。因此，在这项工作中，我们的主要目标是以更高效、有效的方式提出一个用于皮肤病变分割和分类的全自动深度框架。
这项工作提出了一个使用改进的ResNet20-DeepLabV3+和MAKNet100深度模型对皮肤病变进行分割和分类的新颖框架。在分割任务中，设计了一个ResNet20架构作为DeepLabV3+的骨干。在设计的ResNet20架构中，添加了一些具有较小滤波器尺寸的分组卷积层，以提取更多有洞察力的信息。在分类任务中，基于两个定制模型的网络级融合提出了一个新的MAKNet100模型。所提出的网络参数较少，能够提取更多关于病变图像的信息。使用GradCAM可解释人工智能技术（XAI）作为黑箱解释对所提出的模型进行训练和进一步分析。从自注意力层提取特征并传递给分类器进行最终分类。
所提出框架的实验过程在HAM10000、ISIC-2018、ISIC-2019和ISBI-2020数据集上进行，准确率分别为90.5%、88.9%、84.5%和96.35%，在ISIC-2018和HAM10000上获得的最高骰子分数分别为94.63%和96.69%。
所提出的框架在这些数据集上的皮肤病变分割和分类中获得了更高的准确率和精确率。此外，消融研究和与现有技术的比较显示了所提出框架的优势。
