Title: Skin cancer recognition with novel deep learning methodology on mobile platform

Authors: Phillip Ly; Abhishek Verma; Doina Bein

Addresses: Department of Computer Science, California State University, Fullerton, CA, USA ' Department of Computer Science, California State University, Northridge, CA, USA ' Department of Computer Science, California State University, Fullerton, CA, USA

Abstract: The goal of this research is to create mobile applications that can leverage the power of deep learning to detect skin cancer in the early phase and save lives. In this paper we present: 1) novel deep learning-based methodology named as feature extraction with data augmentation and fine-tuning (FEDAFT) to develop compact mobile compatible model that perform effectively in both experimental and real-world situations; 2) our methodology is based on advanced data augmentation, transfer learning, and fine-tuning techniques and obtained top-1 accuracy of 88.35%, which is better than several of the other researches on skin cancer dataset; 3) the model is successfully deployed on the iOS and Android mobile systems; 4) furthermore, we create a composite dataset from several existing datasets for improved recognition accuracy.

Keywords: deep learning; skin cancer; melanoma; neural network; convolutional neural network; CNN; PHDB.

DOI: 10.1504/IJCVR.2026.155533

International Journal of Computational Vision and Robotics, 2026 Vol.17 No.2, pp.216 - 236

Received: 19 May 2023
Accepted: 30 Apr 2024
Published online: 05 Aug 2026 *

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