Title: Sewer shad fly optimisation based efficient skin lesion detection using capsule neural network

Authors: Vineet Kumar Dubey; Vandana Dixit Kaushik

Addresses: Department of Computer Science and Engineering, Harcourt Butler Technical University, HBTU East Campus, Nawabganj, Kanpur, 208002, Uttar Pradesh, India ' Department of Computer Science and Engineering, Harcourt Butler Technical University, HBTU East Campus, Nawabganj, Kanpur, 208002, Uttar Pradesh, India

Abstract: In this research, sewer shad fly optimisation (SSFO) is developed to detect the skin lesion using capsule neural network. HAM10000 dataset is first accessed for input, after which pre-processing is carried out. ROI is segmented using an optimised clustering-based segmentation method based on sewer shad fly optimisation, created as a result of mayfly and moth flame optimisation. The segmented region is sent for feature extraction, which is carried out using both grid-based statistical features and a hybrid ternary pattern. The recovered region is sent to the Capsule Neural Network classifier, uses the sewer shad fly optimisation algorithm to adjust the classifier's weights and bias to accurately detect the skin lesion. The proposed SSFO-CapsNet NN attained the values for TP 90 is 96.45%, 98.00%, 94.28% and while measuring k-fold 10 it attains 95.89%, 98.57%, and 95.76%.

Keywords: capsule neural network; skin lesions classification; sewer shad fly optimisation; SSFO; transfer learning; and resnet-101.

DOI: 10.1504/IJCVR.2026.155190

International Journal of Computational Vision and Robotics, 2026 Vol.17 No.1, pp.32 - 59

Received: 31 Jul 2023
Accepted: 15 Nov 2023
Published online: 29 Jul 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article