Title: Skin cancer detection and segmentation utilising LadderNet by stock exchange white shark optimisation enabled deep learning

Authors: Anuradha Govada; Virendra Singh Kushwah; Ruth Ramya Kalangi; Vimala Shanmugam

Addresses: Department of Computer Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India ' School of Computing Science Engineering and Artificial Intelligence, VIT Bhopal University, Bhopal-Indore Highway Kothrikalan, Sehore, Madhya Pradesh – 466114, India ' Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur District, Andhra Pradesh, India ' Department of Electronics and Communication Engineering, Prathyusha Engineering College, Thiruvallur, Chennai, Tamilnadu, India

Abstract: Skin cancer is one of the most common types of cancer worldwide, with increasing incidence rates over the past few decades. This paper proposes an optimisation-enabled deep learning for skin cancer segmentation and detection. Initially, the input image is pre-processed by a bilateral filter. After that, skin lesion segmentation is performed using LadderNet, which is tuned by Stock exchange trading white shark optimisation (SEWSO). Here, SEWSO is the combination of Stock exchange trading optimisation (SETO) and white shark optimisation (WSO). Moreover, segmented image is allowed through data augmentation which is done by rotation, shifting and random brightness techniques. Thereafter, the feature extraction is achieved to obtain the desired features. At last, the feature vector is subjected to skin cancer detection, which is accomplished by employing SqueezeNet tuned by SEWSO. This approach delivered high accuracy, sensitivity, and specificity of 93.90%, 95.00%, and 94.70%, respectively.

Keywords: skin cancer; SqueezeNet; neural networks; NNs; white shark optimisation; WSO; stock exchange trading optimisation; SETO.

DOI: 10.1504/IJDMB.2026.150974

International Journal of Data Mining and Bioinformatics, 2026 Vol.30 No.1/2, pp.125 - 151

Received: 30 Nov 2023
Accepted: 15 May 2024
Published online: 06 Jan 2026 *

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