• BODDEPALLI KEERTHIVARDHAN
• KAKARLAPUDI SRI VARSHINI
• SAI KUMAR RAJU JULUMURI
• VARSHITH APUROOP KONDRU
• K.L. GANAPATHI REDDY

Skin diseases represent a major global health issue, impacting millions of people each year and potentially leading to severe complications if not diagnosed and treated promptly. Traditionally, diagnosing skin conditions relies on manual examination by dermatologists, a process that can be subjective, time-intensive, and susceptible to human error. Moreover, access to skilled dermatologists is often limited, particularly in remote or underserved regions. To tackle these challenges, advancements in medical technology, especially in the fields of machine learning and computer vision, present promising solutions. The proposed system utilizes Convolutional Neural Networks (CNNs), a deep learning technique, to automate the detection of various skin diseases by analyzing dermoscopic images. This system is designed to classify conditions such as melanoma, eczema, psoriasis, and others, offering valuable support for early detection and effective treatment planning.

Skin Disease Prediction, ResNet Model, Convolutional Neural Networks (CNN), Deep Learning, Dermoscopic Images, Melanoma Detection, Eczema Classification, Psoriasis Diagnosis, Medical Image Processing, Automated Diagnosis.

"An Efficient Resnet Model for Skin Disease Prediction Using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.d405-d408, March-2025, Available :http://www.jetir.org/papers/JETIR2503347.pdf

"An Efficient Resnet Model for Skin Disease Prediction Using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppd405-d408, March-2025, Available at : http://www.jetir.org/papers/JETIR2503347.pdf