A Novel Ensemble Deep Learning Approach for Robust Multi-class Hair Fall Disease Classification

Accurate diagnosis of hair fall diseases is crucial but often relies on limited dermatologist access. A deep-learning ensemble approach for automating hair fall disease classification from dermatological images. Traditional methods involving visual inspection are subjective and error-prone. Our approach leverages state-of-the-art convolutional neural networks (CNNs), including VGG-16, VGG-19, and MobileNet-V2, with an ensemble model averaging strategy to combine predictions and improve performance. Extensive experiments on a curated dataset demonstrate remarkable results, with the ensemble model achieving 99% accuracy on the test set. This automated system has the potential to reduce subjectivity, improve accessibility, aid early diagnosis, and reduce burdens on healthcare systems. A comprehensive analysis is provided, including the dataset, architectures, training process, evaluation metrics, and insights into challenges and future research directions. The source code and models are publicly released: GitHub Repository.