Advanced Dermatology Platform: Deep Learning with VGG19 and DenseNet201, Integrated Chatbot and Community Forum

S. Sarojini Devi; Bora Pavani; M. Pavan Kalyan Varma; Raja Koti. B; Krishna Rupendra Singh; G.B.N. Jyothi; Badugu Samatha

doi:10.52756/ijerr.2024.v45spl.013

Authors

S. Sarojini Devi Department of Computer Science and Engineering, Nadimpalli Satyanarayana Raju Institute of Technology (A), Visakhapatnam, Andhra Pradesh, India https://orcid.org/0009-0002-7292-917X
Bora Pavani Department of Computer Science and Engineering, Vignan's Institute of Information Technology (A), Visakhapatnam, Andhra Pradesh, India https://orcid.org/0009-0003-8163-0193
M. Pavan Kalyan Varma Department of Computer Science and Engineering, Centurion University of Technology and Management, Vizianagaram, Andhra Pradesh, India https://orcid.org/0009-0003-9332-9920
Raja Koti. B Department of Computer Science and Engineering, Gitam School of Technology, GITAM, Visakhapatnam, Andhra Pradesh, India https://orcid.org/0000-0002-2325-2732
Krishna Rupendra Singh Department of Computer Science and Engineering, Vignan's Institute of Engineering for Women(A), Visakhapatnam, Andhra Pradesh, India https://orcid.org/0009-0007-6402-9194
G.B.N. Jyothi Department of Information Technology, S.R.K.R.Engineering College (A), Andhra Pradesh, India https://orcid.org/0009-0007-8353-7602
Badugu Samatha Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fileds, Vaddeswaram, Andhra Pradesh, India7Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fileds, Vaddeswaram, Andhra Pradesh, India https://orcid.org/0000-0003-1353-2797

DOI:

https://doi.org/10.52756/ijerr.2024.v45spl.013

Keywords:

Convolutional Neural Network (CNN) model, Intelligent chatbot, Domain-specific knowledge, Pretrained DCNN, VGG19, DenseNet201, Data management (Firebase)

Abstract

The present online application employs a contemporary artificial intelligence (AI)-driven solution to transform the process of diagnosing skin disorders. This research uses DenseNet201 and VGG19, two of the most advanced DNN architectures, to build a Convolutional Neural Network (CNN). The enhanced predictive models, built with a dataset of 930 photos divided into ten groups and strengthened by data augmentation, produce remarkably accurate predictions for a range of skin conditions. The website's intelligent chatbot is a standout feature; it was built to answer questions about skin diagnoses, treatment options, and more. This chatbot is designed to help users understand their diagnostic results and find their way on the health journey. In addition, it keeps track of users' prediction histories, so they may learn a lot about their skin's health over time and make educated choices about their medical treatments. In addition, by giving people a place to talk about their struggles and get advice from others, the website fosters a supportive community. The emphasis here is on real human connections, which are great for learning from one another and helping one another out. Firebase facilitates efficient data administration for monitoring forecasts and engaging with the community, while Replit and Voice flow support the CNN model, chatbot, and forum, guaranteeing optimal performance. By integrating cutting-edge AI with a user-centric approach, this web application empowers users with the tools, insights, and support necessary for proactive skin health management.

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