Skin Disease Detection using Deep Learning

Skin conditions that become worse by things like pollution, poor food, and different skin tones might conceal early indications of dermatological problems. Time-consuming and prone to human error, traditional diagnostic techniques like biopsies can postpone treatment and raise the risk of conditions like skin cancer. Deep learning-powered hybrid approaches that combine dermatological knowledge with artificial intelligence (AI) present a promising way to diagnose skin diseases more quickly, accurately, and non-invasively while lowering the risk of misdiagnosis and speeding up diagnostic procedures to enhance patient outcomes. The development and evaluation of a deep learning-based system for skin disease diagnosis using convolutional neural networks (CNNs) is the main objective of this study. Images selected from reputable sites like Kaggle make up the dataset HAM10000, which includes images of six different skin conditions: Benign Keratosis, Dermatofibroma, Melanocyte Nevi, Basal Cell Carcinoma, Actinic Keratosis, and Vascular Lesions. There are total 10020 from which 120 testing image and 900 training images in the dataset. In order to increase image quality, the pre-processing stages included scaling image, standardizing pixel values, augmenting the data with flips and rotations, and using noise reduction methods like Gaussian filter. For classification, the 16-layer VGG16 deep learning model which makes use of convolution, pooling, and activation functions like ReLU was used. The system demonstrated an excellent 98% accuracy conventional dataset. The findings demonstrate the system’s ability to reliably identify skin abnormalities even in the presence of changing lighting and background conditions, and they also point to the system’s potential applications in automated skin disease diagnosis and teledermatology.