Recognizing Different Types of Skin Diseases by Using the Efficient Net-B3 Model
Abstract
The skin, as the body's primary defense, faces increased vulnerability to diseases like cancer due to factors such as ozone layer depletion, UV radiation, and infections. Despite advancements in diagnostic tools, accurate and early detection of skin conditions remains a challenge. This study aims to address this gap by proposing a deep learning-based framework using the EfficientNet-B3 model for multi-class classification of eight skin conditions, including malignant and benign types. The approach employs a structured pipeline with data augmentation to enhance the training set, followed by fine-tuning EfficientNet-B3 for improved accuracy. Results show that the optimized model achieves an accuracy and F1-score of 94%, providing a practical and reliable tool for early diagnosis. These findings suggest significant potential for supporting dermatologists, improving patient outcomes, and reducing clinical workload through efficient disease identification.
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