An advanced convolutional neural network for skin disease detection

Abstract

This study refers to an advanced convolutional neural network (CNN) approach for detecting skin diseases, with a focus on distinguishing between cancerous and noncancerous conditions. Using Kaggle's 'Melanoma Skin Cancer Dataset', we addressed class imbalance with rigorous data augmentation, resulting in a balanced dataset of 5000 images per class. Our proposed multilayer CNN architecture was designed and trained using this balanced dataset, with the goal of achieving high disease classification accuracy. To evaluate our approach, we compared our custom CNN architecture to four popular pretrained models: VGG16, ResNet101, InceptionV3, and MobileNetV2. After extensive experimentation and evaluation, we discovered that MobileNetV2 consistently outperformed all other models, achieving an impressive 98.95% accuracy. This result highlights the effectiveness of MobileNetV2 in accurately detecting skin diseases. This result shows MobileNetV2's effectiveness in accurately detecting skin diseases, which outperforms even our proposed CNN architecture. These findings focus on the importance of selecting the right model architecture for skin disease detection tasks. The superior performance of MobileNetV2 shows its suitability for real-world applications requiring accurate and efficient disease diagnosis. Overall, this study adds valuable insights to the development and evaluation of CNN-based approaches for skin disease detection, with implications for improving diagnostic accuracy and patient outcomes.