Dermatological Diagnosis using EfficientNet

Skin disease needs proper diagnosis, but the conventional approach is usually unreliable. CNNscan be used in skin image analysis, and such datasets as ISIC 2019 make AI diagnosis more powerful. This studypresents an advanced machine learning approach for skin disease classification using Convolutional NeuralNetworks (CNNs), leveraging the ISIC 2019 Archive—a comprehensive dataset featuring high-resolution skinlesion images and associated metadata. The project involved extensive data preprocessing, normalization,augmentation, and Exploratory Data Analysis (EDA) to address dataset imbalances and reveal key insights. Arobust CNN architecture was developed to extract intricate features from skin lesion images, essential for preciseclassification. Additionally, a Multimodal Convolutional Network Model was proposed, integrating image data withstructured patient information to enhance diagnostic accuracy. Experimental results demonstrated the model'spotential for clinical application, surpassing traditional CNN performance in accuracy and reliability. Thisresearch highlights the transformative role of deep learning in dermatology and lays the groundwork for futureadvancements, including model optimization, refined performance metrics, and the development of a user-friendlyweb application to facilitate widespread adoption in healthcare settings.