Hair and Scalp Disease Detection Using Machine Learning and Image Processing
Studysummary This study developed a deep learning model using the VGG architecture to predict hair disorders and provide tailored therapeutic suggestions, showing reliable recognition of conditions like dandruff, fungal infections, and alopecia by analyzing images of hair and scalp.
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This study developed a deep learning model based on the VGG architecture to predict hair disorders and provide tailored therapeutic suggestions. By using a convolutional neural network (CNN) and transfer learning, the model was trained on a diverse set of images to accurately recognize hair and scalp conditions such as dandruff, fungal infections, and alopecia. The AI-based system demonstrated high sensitivity and specificity, reducing false-positive and false-negative outcomes, and has the potential to transform dermatology by offering prompt and accurate diagnoses and customized treatment recommendations. This research highlights the role of artificial intelligence in enhancing healthcare outcomes in dermatology and skincare.
