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Context: Some scalp infections may be treatable if diagnosed early. Some but not all diseases may go on their own. Only an expert physician can detect the illness by visual observation. In some cases, early disease detection is beneficial for dermatologists to initiate the treatment. An early scalp inspection includes a dermatoscopic examination of the scalp for inflammation, itching, localized lesion, dandruff, follicular flakes, louse eggs (nits), and a scalp biopsy. Besides visual observation, the patient can undergo blood and hormone tests to detect the exact disease. Unfortunately, most hair and scalp diseases are diagnosed in advanced stages, which complicate the treatment options. All these factors lengthen the diagnosis and treatment process. Therefore, researchers are putting more effort into developing different mechanisms for the early detection of hair and scalp diseases. Disease detection using machine learning approaches is gaining popularity in health informatics. Many skin and scalp-related diseases can be detected using images of infected regions within a few seconds. In one study by Choudhary et al. [18], a framework is developed to differentiate alopecia areata from healthy hair. They obtained 200 healthy hair images from the figaro1K dataset and 68 alopecia areata hair images from DermNet. After a series of enhancement and segmentation, three key features were Although early-stage detection of hair and scalp-related diseases is the key to the treatment process, hair loss and scalp diseases can often go undetected due to a lack of awareness and a lengthy diagnosis test. An AI-based application might pave the way to facilitate early disease detection. In this study, we developed a machine learning model to accurately predict three hair and scalp-related diseases: alopecia, folliculitis, and psoriasis by feeding 150 preprocessed image data into a 2-D convolutional neural network model. After using 70% of the data to train the model, we analyzed remaining 30% of images for testing our model. After subsequent training, the model gave an overall 96.2% training accuracy on the training data and 91.1% validation accuracy for the test data, with a high precision and recall scores for each disease type. We have also provided our dataset with this study. Our proposed system would assist dermatologists and patients with a better understanding of disease classification and initiating early treatment options for the three most frequently occurred hair and scalp diseases.

Question: According to the author most hair and scalp disease is diagnosed in advanced stages. What could be the possible reason behind this ? How can we sensitize people for early diagnosis of hair disease?
Although early-stage detection of hair and scalp-related diseases is the key to the treatment process, hair loss and scalp diseases can often go undetected due to a lack of awareness and a lengthy diagnosis test.  An AI-based application might pave the way to facilitate early disease detection. Disease detection using machine learning approaches is gaining popularity in health informatics. Therefore, AI-based approach for fast detection is a good way to make people go for the early diagnosis of the hair diseases.