Optimized Deep Learning Approach For Dermatological Condition Classification Using Efficient Net

Abstract

Skin disorders are becoming more common, but because of the variety of conditions and the complexity of medical imaging, it is still difficult to provide an accurate diagnosis.  Generative Adversarial Networks (GANs) are the mainstay of current systems for predicting skin diseases; yet, they suffer from instability and inconsistent training.  Furthermore, when the generated images fall short of accurately capturing real-world variability, the dependence on artificial data production frequently leads to less accurate forecasts.  The suggested approach uses EfficientNetB0, a deep learning model tailored for medical image processing, to get over these restrictions.  EfficientNetB0 uses a hybrid scaling approach that equalizes depth, width, and resolutions, allowing for highly precise extraction of characteristics. It is perfect for classifying skin diseases because of its lightweight construction, which enables faster processing without sacrificing speed. Utilizing EfficientNetB0, the system lowers the risk of misclassification, increases early detection, and improves diagnostic accuracy—all of which contribute to improved patient outcomes in clinical practice.