Open Access

ARTICLE

Enhanced Tunicate Swarm Optimization with Transfer Learning Enabled Medical Image Analysis System

Nojood O Aljehane^*

Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia

* Corresponding Author: Nojood O Aljehane. Email:

Computer Systems Science and Engineering 2023, 47(3), 3109-3126. https://doi.org/10.32604/csse.2023.038042

Received 24 November 2022; Accepted 24 February 2023; Issue published 09 November 2023

Abstract

Medical image analysis is an active research topic, with thousands of studies published in the past few years. Transfer learning (TL) including convolutional neural networks (CNNs) focused to enhance efficiency on an innovative task using the knowledge of the same tasks learnt in advance. It has played a major role in medical image analysis since it solves the data scarcity issue along with that it saves hardware resources and time. This study develops an Enhanced Tunicate Swarm Optimization with Transfer Learning Enabled Medical Image Analysis System (ETSOTL-MIAS). The goal of the ETSOTL-MIAS technique lies in the identification and classification of diseases through medical imaging. The ETSOTL-MIAS technique involves the Chan Vese segmentation technique to identify the affected regions in the medical image. For feature extraction purposes, the ETSOTL-MIAS technique designs a modified DarkNet-53 model. To avoid the manual hyperparameter adjustment process, the ETSOTL-MIAS technique exploits the ETSO algorithm, showing the novelty of the work. Finally, the classification of medical images takes place by random forest (RF) classifier. The performance validation of the ETSOTL-MIAS technique is tested on a benchmark medical image database. The extensive experimental analysis showed the promising performance of the ETSOTL-MIAS technique under different measures.

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Keywords

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