Abstract
hair-loss and Scalp related issues are effected for many people due to factors such as stressing, medication ,genetics, heredity, hormonal changes .difference between regular hair fall underline conditions often presents challenges, leading to delayed diagnosis and potentially more serious health issues .To address this issues ,using neural network-based applications. which have revolutionized healthcare and health informatics .This study focuses on predicting three primary scalp disease are identified such as : Psoriasis, Alopecia areta and folliculitis and hair loss stages from stage 0 to stage 4 to detect this utilizing a convolutional neural network (CNN) and types using restnet50,vgg16 ,vgg19 ,efficientnet .using this architecture to comparison between this types to predict accuracy model, help to more confidenceparticular disease effected and healthcare providers to offer personalized treatment plans and preventive measures tailored to each patient's unique circumstances .By addressing this hair-loss andscalp related issues by seeing this result we can go trough dermatology by this we can reduce time and in this project any patient can go with testing and they can go with suggestion what descripted and this helps to reduce cost as this product is home remedies , this is natural remedies their will be won’t harm any chemical side effect . the model aids in preventing condition progression, ultimately reducing the strain of healthcare organization and this can lead ultimately better quality of life of users.
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