CNN and Transfer Learning Methods for Enhanced Dermatological Disease Detection

Abstract

Since skin diseases generally badly affect lives, the earlier and more accurate the diagnosis, the
better the chances of effective treatment and a better prognosis. Deep learning applications, especially CNNs,
has revolutionized the domain of disease classification, significantly increasing the accuracy of diagnoses for
such common conditions and facilitating early interventions. The huge success behind the ongoing project
motivated advancements of the developing in CNN techniques towards detection of skin disease by using the
concept of Transfer Learning. So, the older models, which had employed it for detecting Eczema and Psoriasis
based on the architectures involving deep CNNs. The Inception ResNet v2 architecture improved the accuracy
of that model, with some practical implementations via smartphone integration and web server integration.
Some of those innovations are as follows in our project. The earlier work used different CNN architectures.
Our approach involved Transfer Learning with a pre-trained ResNet50 model to try to improve performance
and efficiency using features learned from large-scale datasets. This reduce the complexity and enhance the
accuracy. Besides Transfer Learning adaptation, our project encompasses elaborate preprocessing techniques
like resizing, normalization, and data augmentation in fine-tuning the dataset for further model fine-tuning. It
has 97.6% accuracy, 95% precision, 99.4% recall, and 97.4% F1-score. rad-CAM techniques have been
employed to visualize and interpret model predictions. This final model has been a pragmatic and accessible
tool for early detection and diagnosis of skin disease. The feature here is an attempt to provide a more accurate,
efficient, and user-friendly diagnostic solution through the incorporation of advanced methods of Transfer
Learning and visualization.