Deep Belief Neural Network Framework for an Effective Scalp Detection System Through Optimization

Abstract

In an era where technology rapidly enhances various sectors, medical services have greatly benefited, particularly in tackling the prevalent issue of hair loss, which affects individuals' self-esteem and social interactions. Acknowledging the need for advanced hair and scalp care, this paper introduces a cost-effective, tech-driven solution for diagnosing scalp conditions. Utilizing the power of deep learning, we present the Grey Wolf-based Enhanced Deep Belief Neural (GW-EDBN) method, a novel approach trained on a vast array of internet-derived scalp images. This technique focuses on accurately identifying key symptoms like dandruff, oily scalp, folliculitis, and hair loss. Through initial data cleansing with Adaptive Gradient Filtering (AGF) and subsequent feature extraction methods, the GW-EDBN isolates critical indicators of scalp health. By incorporating these features into its Enhanced Deep Belief Network (EDBN) and applying Grey Wolf Optimization (GWO), the system achieves unprecedented precision in diagnosing scalp ailments. This model not only surpasses existing alternatives in accuracy but also offers a more affordable option for individuals seeking hair and scalp analysis, backed by experimental validation across several performance metrics including precision, recall, and execution time. This advancement signifies a leap forward in accessible, high-accuracy medical diagnostics for hair and scalp health, potentially revolutionizing personal care practices.