Hair and Scalp Inspection System Using Deep Learning Based Analysis

The Artificial intelligence integration into dermatology has transformed the scope of diagnosis and management of hair and scalp conditions, addressing the drawbacks associated with conventional methods that tend to be subjective, labor-intensive, and vulnerable to human error. Hair and scalp conditions such as psoriasis, alopecia, head lice, and fungal infections significantly affect the health and wellbeing of an individual; therefore, early detection and intervention are of paramount importance. This paper presents an automated hair and scalp inspection system, using deep learning techniques based on convolutional neural networks (CNNs), to classify high-resolution images into 11 categories that include10 common disorders and healthy hair. It is a noninvasive, accurate, and efficient diagnostic approach that can be useful for healthcare professionals and individuals. A curated dataset of $\mathbf{1 3 , 2 0 0}$ high-resolution RGB images was preprocessed to enhance variability using augmentation techniques, thus making the model more robust and generalized to real- world scenarios. Fine-tuned CNN models that utilize residual learning were used to capture intricate patterns and textures in the images. Hyperparameter optimization was performed to tune learning rates, batch sizes, and dropout rates for the optimal performance of the model. The evaluation of the system showed a great classification accuracy of $93.71 \%$, proving that it can classify a wide range of hair and scalp conditions with reliability. Adding residual learning enhanced the model’s ability to examine complex image patterns, increasing its diagnostic accuracy. This automated system is scalable, reliable, and efficient in replacing the traditional diagnostic methods with minimum effort in manual labor and less human error. It is an early detection and diagnosis practical solution in the clinical and personal settings that will pave the way for AI integration in healthcare. The results of this study reveal the transformative potential of deep learning in dermatology, where it has shown its capability to enhance the accuracy of diagnosis and streamline medical processes. By setting the stage for future improvements in automated diagnostics, this research underscores the wider ramifications of artificial intelligence in the revolutionization of healthcare delivery and outcomes.