Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, India.
Abstract
Skin disease diagnosis is a complex task requiring specialized expertise due to the diversity of dermatological conditions. Convolutional Neural Networks (CNNs) offer a promising solution by automating diagnosis through pattern recognition in medical images. This study explores CNNs’ effectiveness in skin disease detection, focusing on the impact of data augmentation on model performance. Initially, a CNN model achieved 76% training accuracy and 85% testing accuracy. With data augmentation—using techniques like rotation, flipping, and scaling—these figures improved to 81% and 87%, respectively. These transformations expanded the dataset, enhancing the model’s ability to generalize by mimicking real-world conditions. This process combats overfitting and helps the model learn robust features crucial for accurate diagnosis. The CNN architecture utilized multiple convolutional layers to extract essential features from images, followed by pooling layers to reduce dimensions and prevent overfitting. Fully connected layers then consolidated these features for final classification. The model’s evaluation involved metrics such as accuracy, precision, recall, and F1 score, highlighting the augmented model’s superior performance. This research underscores the significance of data augmentation and pre-processing in developing reliable diagnostic tools. The study’s findings suggest that CNNs can aid comdermatologists by providing accurate, efficient diagnoses, particularly valuable in areas with limited access to healthcare expertise. Future work aims to refine these models further, ensuring their interpretability and integration into clinical settings, thereby enhancing patient care and outcomes.
Keywords: Augmentation, Convolutional Neural Network (CNN), Data pre-processing, Deep learning, Dermatology, Diagnostic accuracy, Healthcare innovation, Machine learning, Medical imaging, Skin disease.
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