XAI-Driven Skin Disease Classification: Leveraging GANs to Augment ResNet-50 Performance
PositiveArtificial Intelligence
- A new study has introduced a Computer-Aided Diagnosis (CAD) system that utilizes Deep Convolutional Generative Adversarial Networks (DCGANs) to augment data for training a fine-tuned ResNet-50 classifier, achieving an impressive accuracy of 92.50% in classifying seven skin disease categories. The integration of Explainable AI techniques, LIME and SHAP, enhances the transparency of predictions based on clinically relevant features.
- This development is significant as it addresses critical challenges in dermatological diagnostics, such as subjective assessment and data imbalance in existing datasets like HAM10000. By improving classification accuracy and interpretability, the CAD system could lead to better patient outcomes and more reliable diagnostic processes in clinical settings.
- The advancement of AI in medical diagnostics reflects a broader trend in leveraging deep learning technologies to enhance accuracy and transparency across various medical fields. Similar approaches are being explored in other areas, such as lung cancer classification and brain tumor detection, highlighting the growing importance of explainability in AI systems to ensure trust and efficacy in healthcare applications.
— via World Pulse Now AI Editorial System
