Paper Title
DERM-Detect: Skin Disease Classification Using CNN

Abstract
Treating skin diseases is imperative for several reasons, as skin serves as the body's first line of defence against external threats. Timely and effective treatment is crucial to prevent the progression of skin conditions, alleviate symptoms, and mitigate potential complications. Therefore, prioritizing and investing in accessible and effective treatments for skin diseases is fundamental to ensuring the overall health and quality of life for individuals and the broader population.The growing demand for advanced techniques in the classification of skin diseases has prompted a search for accessible and accurate solutions. While Artificial Intelligence and Machine Learning models have shown promise, many are still proprietary and inaccessible to the general public. In response to this gap, we propose a Convolutional Neural Networks (CNN)-based model for identifying various skin diseases, addressing the need for readily available and efficient diagnostic tools. Our study offers a multiclass CNN model for identifying 14 different types of skin disorders. The fundamental goal of this initiative is to make skin disorders more easily and accurately detectable, hence improving diagnostic accuracy. Keywords - Convolutional Neural Networks, Artificial Intelligence, Machine Learning, Multiclass, Accuracy