Optimizing Dermatological Image Classification Using Efficient Convolutional Neural Network Architecture

Skin diseases represent a global healthcare challenge because of their frequent occurrence and complex diagnosis. However, despite clinical advances, accurately identifying dermatological lesions remains difficult due to significant intra-class variability, overlapping visual patterns, and reliance on clinician expertise. In this study, it presents a complete overview of a number of state-of-the-art CNN architectures as they apply to multiclass classification of skin diseases. The study introduces an overview of the common skin diseases and discuss the fundamentals of deep learning for medical image analysis. The study proceeds to introduce the dataset used in this work and provide a brief description of the two diagnostic groups identified for evaluation. A range of CNN models which comprise GoogLeNet, Inception-V3, Inception-V4, ResNet-50, Xception, MobileNet, ResNeXt-50, AlexNet, VGG-16, and VGG-19 were trained and tested in terms of accuracy, loss, FLOPs, and epoch runtime. The experimental findings suggest that Xception performs constantly at the highest level, with an accuracy of more than 98% and low validation loss, whereas lightweight models such as MobileNet-V3 provide a competitive outperformance with minimum computational cost. These findings demonstrate the potential of modern CNN architectures to enhance efficient and accurate dermatological diagnosis and offer guidance for selecting appropriate architectures for clinical and real-time deployment.

@article{Ladrham2025,
  title     = {Optimizing Dermatological Image Classification Using Efficient Convolutional Neural Network Architecture},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Khalil Ladrham and Hicham Gueddah},
  doi       = {10.14569/IJACSA.2025.0161252},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161252}
}