Authors - Anshuman Prajapati, Madhav Desai, Priyanka Patel Abstract - Analysis of facial skin conditions is essential for both dermatological and cosmetic evaluation; however, inter-class similarity and localized texture variations make multi-label classification of characteristics like wrinkles, dark circles, enlarged pores, hyperpigmentation, pimples, and fine lines difficult. The effectiveness of transfer learning for this task is examined in this paper, and an attention-enhanced framework based on EfficientNet-B0 is proposed. In order to highlight the importance of pre-trained feature representations, we first assess a bespoke convolutional neural network (CNN) as a baseline. Using the Convolu tional Block Attention Module (CBAM), which combines channel and spatial attention processes to enhance discriminative feature localization while maintain ing computational efficiency, we build upon this by using EfficientNet-B0 as the transfer learning backbone. According to experimental data, our CBAM augmented EfficientNet achieves better class-balanced performance in macro-F1 score than both the baseline EfficientNet and the bespoke CNN. Consistent in creases are confirmed by per-class analysis and confusion matrices, even for dif ficult settings. Additionally, Grad-CAM visualizations show that by concentrat ing activation on pertinent facial regions, the attention mechanism improves in terpretability. These results imply that a promising avenue for multi-label derma tological image analysis is attention-guided transfer learning.
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