High frequency ultrasound image analysis using AI in dermatological assessment

Effective dermatological diagnosis and treatment depend on the early and precise detection of skin lesions, especially melanoma. Scalability in clinical practice is limited by the time-consuming, subjective, and expert-experience-reliant nature of traditional manual segmentation techniques. This study uses the publicly available ISIC 2017 dataset to demonstrate a deep learning-based method for multiclass skin lesion segmentation. To maintain spatial resolution and contextual information, we integrate encoder-decoder designs with skip connections in our sophisticated convolutional neural network architecture, which is tailored for semantic segmentation tasks. To facilitate supervised training for lesion boundary delineation, the dataset consists of dermoscopic images annotated with pixel-level ground truths. Key metrics like training loss, validation loss, dice coefficient, and intersection over union (IoU) are used to evaluate performance across several training epochs. Improved prediction accuracy and strong generalization are indicated by the results, which show a steady decline in loss values and a noticeable improvement in Dice and IoU scores. Melanoma, nevus, and seborrheic keratosis are among the lesion types that the model successfully segments, performing well in every class. The model’s dependability is highlighted by the thorough visualization of the training and validation curves, which shows no discernible overfitting. Thus, the suggested system offers a scalable, non-invasive diagnostic support tool that may find use in teledermatology and automated skin lesion analysis. Our results support the feasibility of using deep learning models to enhance dermatological evaluations and enhance the detection of melanoma in its early stages. Future research might focus on real-time clinical deployment and multimodal fusion.