Mobile-Based Skin Cancer Classification System Using Convolutional Neural Network

Introduction: Skin cancer is a growing concern worldwide, often exacerbated by limited awareness and accessibility to diagnostic tools. Early detection is critical for improving survival rates and patient outcomes. This study developed a convolutional neural network (CNN) algorithm integrated into a mobile application to address this issue. Methods: The researchers employed an agile methodology to design and implement a CNN-based skin cancer detection system using the VGG16 architecture. A dataset of skin cancer images from the International Skin Imaging Collaboration (ISIC) was used, consisting of 1,500 images divided into six classes. The model was trained on 1,200 images and tested on 300 images. Preprocessing steps included resizing images to 224x224 pixels, normalization, and image augmentation to enhance model generalization. Results: The trained model achieved a test accuracy of 86.67% in classifying skin cancer types, with the highest performance for healthy skin (100% accuracy) and melanoma (98% recall). The mobile application allows users to upload or capture images of skin lesions and receive automated classification results, including lesion characteristics such as asymmetry, border, color, and diameter. Additional features include user authentication and history tracking, enhancing usability and accessibility. Conclusions: The study successfully developed a reliable CNN-based skin cancer detection system integrated into a user-friendly mobile application. The application provides a valuable tool for early detection and awareness of skin cancer. Future work should focus on clinical validation, expanding the dataset to include diverse populations, and optimizing the system for mobile deployment