Psoriasis support system based on semantic segmentation AI models - Lopes, Toni Marcos Silva (2025)
Psoriasis is a chronic inflammatory skin disease characterized by the presence of lesions that vary in shape, size, and color. Accurate segmentation of these lesions from clinical images is crucial for effective diagnosis and treatment monitoring. However, the variability in image dimensions, lighting conditions, and the presence of noise in real-world datasets complicates the segmentation task. Additionally, available datasets for psoriasis lesion segmentation are often small, making it challenging to train deep learning models effectively without overfitting. The project aims to enhance the segmentation accuracy of psoriasis lesions by employing a range of AI models and optimizing their performance through the application of advanced pre-processing techniques and data augmentation strategies. The AI models tested include Mask R-CNN, U-Net, YOLOv8n, FCN, DeepLabV3+, BiSeNet, HRNet, PSPNet, and SegNet. Pre-processing techniques, such as bilateral smooth filters and adaptive median filters, were applied to enhance image quality and reduce noise. Furthermore, data augmentations, including random adjustments in brightness, contrast, cropping, rotation, flipping, and scaling, were used to simulate real-world variations and increase the robustness of the models. These augmentations were carefully replicated from related works to mimic their experimental setups. The evaluation of multiple AI models for psoriasis lesion segmentation demonstrated that applying effective pre-processing and data augmentation techniques significantly improves model performance. Among the tested models, FCN achieved the highest F1 score of 0.889, using a bilateral smooth filter and translation augmentations. U-Net and SegNet followed closely, with U-Net reaching an F1 score of 0.885 and SegNet 0.880, both benefiting from similar pre-processing and augmentation strategies. Mask R-CNN and BiseNet also showed competitive results, underscoring the importance of carefully selected augmentations that mimic real-world variability. These findings highlight the value of pre-processing and augmentation in enhancing model generalization, particularly in small, diverse datasets. In addition to the AI-based segmentation work, the project also incorporates a mobile application designed to assist patients in tracking their psoriasis lesions over time. This application enables users to monitor whether lesions are improving or worsening, providing valuable insights for deciding when to consult a dermatologist. By helping patients recognize early signs of deterioration, the application plays a crucial role in supporting more proactive and informed healthcare decisions.