Research Article
Deep Learning Strategies for Multiclass Skin Disease Classification
@INPROCEEDINGS{10.1007/978-3-031-77075-3_18, author={Nakka Lokeswara Satya Venkat and Masina Naga Vijaya Tejasri and Padma Jyothi Uppalapati and V. V. R. Maheswara Rao and V. S. S. Lakshmi Sripada and P. Sita Rama Murty}, title={Deep Learning Strategies for Multiclass Skin Disease Classification}, proceedings={Cognitive Computing and Cyber Physical Systems. 5th EAI International Conference, IC4S 2024, Bhimavaram, India, April 5--7, 2024, Proceedings, Part-I}, proceedings_a={IC4S}, year={2025}, month={2}, keywords={Skin Disease Medicine Convolutional Neural Network Fusion model VGG16 EfficientNetB2 ResNet50 Xception}, doi={10.1007/978-3-031-77075-3_18} }- Nakka Lokeswara Satya Venkat
Masina Naga Vijaya Tejasri
Padma Jyothi Uppalapati
V. V. R. Maheswara Rao
V. S. S. Lakshmi Sripada
P. Sita Rama Murty
Year: 2025
Deep Learning Strategies for Multiclass Skin Disease Classification
IC4S
Springer
DOI: 10.1007/978-3-031-77075-3_18
Abstract
Skin diseases represent a significant global health challenge, necessitating accurate and timely diagnosis for effective treatment. In this paper, we delve into the intricate realm of skin disease classification, leveraging advanced deep learning techniques. Skin diseases, ranging from common conditions like eczema and atopic dermatitis to potentially life-threatening melanoma, pose complex diagnostic challenges due to their varied visual characteristics. Accurate classification demands the ability to discern subtle patterns, textures, and features within medical images. Our paper introduces a novel methodology that includes the integration of our proposed model into the existing deep learning frameworks. Unlike conventional approaches involving the replacement of output layers, our model maintains the integrity of pre-trained structures. It achieves this by utilizing a blend of VGG16 and EfficientNet B2, incorporating dense activations for improved regularization. This novel approach seeks to improve the model’s flexibility to various skin disease patterns while maintaining accurate classification without changing the pre-trained models’ fundamental design. The model’s performance is evaluated in-depth using a variety of criteria, including accuracy, precision, recall, and F1 score. The outcomes of this analysis unveil distinct F1 scores for each disease class across different models, thereby illuminating the models’ strengths and limitations in the context of disease classification.
