Automated Classification of Before-and-After Botox Faces Using Advanced Deep Learning Models
Abstract
Botox injections are a popular, non-invasive treatment in facial aesthetics, used to reduce wrinkles and achieve a youthful appearance. Accurate evaluation of Botox’s efficacy is essential in clinical settings, yet it is often subject to subjective interpretation. This study presents an automated, objective approach for classifying pre- and post-Botox facial images using advanced deep-learning models, including MobileNet, ResNet50, and InceptionV3. The models were trained on a diverse dataset of facial images, achieving high performance in classifying treatment outcomes. InceptionV3 demonstrated the highest accuracy (89.27%), precision (91.15%), and recall (92.27%), with statistically significant differences across models (p < 0.05) for all metrics. While InceptionV3 and ResNet50 excelled in accuracy and recall, MobileNet offered a computationally efficient option suited for real-time applications.
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