Hairfall Hairfall and Scalp Disease Detection Using Deep Learning and AI Thesis Description
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Millions of people worldwide suffer from common health issues like hair loss and scalp disorders, which can cause psychological discomfort and, in extreme situations, necessitate medical attention. Numerous factors, such as genetics, lifestyle, environmental effects, and underlying health disorders, contribute to the complexity in diagnosing these illnesses. Because of their diverse and erratic presentations, dermatologists and trichologists encounter significant difficulties in correctly diagnosing and treating these conditions. Recent developments in artificial intelligence (AI) have opened up new avenues for improving dermatology diagnostic accuracy. This study makes use of cutting edge deep learning and machine learning methods to better accurately identify scalp conditions and hair loss trends. Random Forest, K Nearest Neighbors (KNN), Logistic Regression, Convolutional Neural Network (CNN), Simple Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Artificial Neural Network (ANN) are among the models whose performance we assess. Standard classification metrics including Accuracy, Precision, Recall, F1-Score, and Area under the Curve (AUC) are used to gauge each model's efficacy. With an AUC of 0.975, the results show that Logistic Regression has the best accuracy for class separation, demonstrating its potent ability to distinguish between circumstances. Furthermore, with an accuracy of 0.15, precision of 0.067, recall of 0.15, and F1-Score of 0.08, the RNN model with a TANH activation function was the best performer on a number of criteria. By addressing the vanishing gradient problem, a prevalent difficulty in recurrent models, the TANH function which maps inputs between -1 and 1 proves beneficial and improves predictive stability. These results highlight how AI-driven models have the potential to greatly enhance therapy planning and diagnostic precision in the management of hair loss and scalp disorders. AI has the potential to be a crucial tool in dermatology with future development, enabling more precise, effective, and early detection that will ultimately improve patient outcomes and enable more focused therapies.
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