Artificial Neural Networks Algorithm for Diagnosis of Human Hair loss
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In this book, artificial neural networks (ANNs) are being used to diagnose hair loss in patients. An autoimmune condition known as Alopecia Areata (AA) results in hair loss in the affected area. The most recent figures from throughout the world show that AA affects 1 in 1000 persons and has a 2% incidence rate. For instance, classification is important in the field of medicine because one of the doctor's main objectives is to establish whether or not a patient has a condition. The objective of this study is to evaluate the accuracy of neural networks for alopecia detection in human subjects. H...
In this book, artificial neural networks (ANNs) are being used to diagnose hair loss in patients. An autoimmune condition known as Alopecia Areata (AA) results in hair loss in the affected area. The most recent figures from throughout the world show that AA affects 1 in 1000 persons and has a 2% incidence rate. For instance, classification is important in the field of medicine because one of the doctor's main objectives is to establish whether or not a patient has a condition. The objective of this study is to evaluate the accuracy of neural networks for alopecia detection in human subjects. Healthy Hairs (HHs) and Alopecia Areata (AA) have an IA classification framework that will be subject to IP, including CLAHE enhancement and segmentation. Then, to increase the precision of the proposed framework, data augmentation (DA) was employed to generate further data. The VGG- 19 pre-trained CNN model was then used to extract features. To create a machine learning model, the Support Vector Machine (SVM) classification approach is used. The remaining images in the series were used for testing. The suggested VGG-SVM was demonstrated to be 98.31% accurate in the simulation.
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