Title: Hybrid fuzzy-based Shepard convolutional maxout network-based skin cancer detection

Authors: Pakutharivu Panneerselvam; Santhi Krishnan; Chellatamilan Thiyagarajan; Ramanathan Lakshmanan

Addresses: Department of Computer Science, Anna Adarsh College for Women, Chennai – 40, Tamil Nadu, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India

Abstract: Skin cancer arises from the uncontrolled growth of skin cells. Timely detection can significantly lower mortality rates and improve patient outcomes. However, diagnosing melanoma can be challenging due to its similarity to benign lesions. This study introduces a fuzzy Shepard convolutional neural network (FSCMN) for detecting skin cancer in images. Initially, the pre-processing is done by using a bilateral filter. Then, the skin lesions are segmented by using recurrent prototypical network (RP-Net). Next, features are extracted using convolutional neural networks (CNN), local vector pattern (LVP), grey level co-occurrence matrix (GLCM) texture features, and entropy-based local directional texture pattern (LDTP). Finally, skin cancer detection is performed by proposed FSCMN, which integrates fuzzy logic, Shepard convolutional neural network (ShCNN), and deep maxout network (DMN). The FSCMN approach achieved impressive results, with accuracy, true positive rate (TPR), and true negative rate (TNR) of 0.928, 0.939, and 0.914.

Keywords: fuzzy concept; recurrent prototypical network; RP-Net; local vector pattern; LVP; Shepard convolutional neural network; ShCNN; deep maxout network; DMN; grey level co-occurrence matrix; GLCM; local directional texture pattern; LDTP.

DOI: 10.1504/IJAHUC.2025.147564

International Journal of Ad Hoc and Ubiquitous Computing, 2025 Vol.49 No.3, pp.165 - 187

Received: 27 May 2024
Accepted: 26 Nov 2024
Published online: 21 Jul 2025 *

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