Title: Mobile Application of Convolutional Neural Networks for Melanoma Classification
Abstract: Skin cancer is one of the most common types of cancer in terms of incidence around the world. Melanoma, in particular, manifests as visible pigmented skin lesions, often a result of sun overexposure. Despite the fact of rapid development of dermatology, delays in diagnosing skin diseases and poor access to their treatment are occurring in modern medicine.Several studies have demonstrated the potential of CNN in developing accurate and noninvasive screening methods for skin cancer. Nowadays, a limited number of mobile applications are identified that involve deep learning for the scanning and screening of skin diseases.This study simultaneously proposes a CNN architecture with a non-standard activation function, as well as presents the results of the proposed CNN model in terms of various metrics, such as accuracy, recall, specificity, precision, and so on. The software development is implemented in the Flutter framework, focusing on the Waterfall model. The key components of the mobile application, including the splash screen, main page, navigation, health-related features, and the "About Us" section, are described.The results of this study demonstrate that the proposed CNN model achieved 81.53% in terms of accuracy. The image augmentation techniques employed contribute to improved model generalization and robustness. By combining the capabilities of CNNs with user-friendly mobile interfaces, individuals can gain easy access to melanoma assessment, promoting proactive healthcare practices.
Keywords: Mobile application prototype;skin cancer;CNN;melanoma classification;Flutter;dataset;image augmentation;training;validation;accuracy
Review score: Moderate
Review comment: The manuscript presents an approach to addressing a critical healthcare challenge: the early detection and diagnosis of skin cancer, specifically melanoma. By leveraging the capabilities of Convolutional Neural Networks (CNN) and integrating these with a user-friendly mobile application developed using the Flutter framework, this study offers a timely contribution to the field of digital health and dermatology. The development of a non-invasive screening method for skin cancer through mobile technology represents a timely and important advancement given the increasing incidence of skin cancer globally and the challenges associated with delays in diagnosis and treatment access.

The introduction of a CNN architecture with a non-standard activation function showcases the novel technical approach taken by the researchers. The comprehensive evaluation of the model's performance, using a variety of metrics including accuracy, recall, specificity, and precision, provides a clear and convincing argument for the model's effectiveness. With an accuracy of 81.53%, the proposed model demonstrates a promising level of performance for the screening of skin lesions indicative of melanoma. Furthermore, the use of image augmentation techniques to improve model generalization and robustness is a noteworthy aspect of the study, indicating a thoughtful approach to addressing common challenges in machine learning-based diagnostics.