A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

Abstract

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability.

KEYWORDS:

• Machine-learning
• image recognition
• binary classification
• Create ML
• malignant lesion
• software engineering

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Aron Benedek Szabo

Aron Benedek Szabo graduated from the BSc in Computer Engineering at the University of Dunaújváros. He has several years of experience in software development and team leadership in both Hungarian and international projects. He is currently working as a project lead development engineer.

Jozsef Katona

Jozsef Katona is an Associate Professor at University of Dunaujvaros, Obuda University, John von Neumann University and Budapest Business University in Hungary. He holds a Ph.D. degree in Computer Engineering. His research areas are human-computer interfaces, brain-computer-interfaces, eye-tracking analysis, programming and software development.