Open Access
ARTICLE
Intelligent Beetle Antenna Search with Deep Transfer Learning Enabled Medical Image Classification Model
Mohamed Ibrahim Waly*
Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah,
11952, Saudi Arabia
* Corresponding Author: Mohamed Ibrahim Waly. Email:
Computer Systems Science and Engineering 2023, 46(3), 3159-3174. https://doi.org/10.32604/csse.2023.035900
Received 09 September 2022; Accepted 21 December 2022; Issue published 03 April 2023
Abstract
Recently, computer assisted diagnosis (CAD) model creation has
become more dependent on medical picture categorization. It is often used
to identify several conditions, including brain disorders, diabetic retinopathy,
and skin cancer. Most traditional CAD methods relied on textures, colours,
and forms. Because many models are issue-oriented, they need a more substantial capacity to generalize and cannot capture high-level problem domain
notions. Recent deep learning (DL) models have been published, providing
a practical way to develop models specifically for classifying input medical
pictures. This paper offers an intelligent beetle antenna search (IBAS-DTL)
method for classifying medical images facilitated by deep transfer learning.
The IBAS-DTL model aims to recognize and classify medical pictures into
various groups. In order to segment medical pictures, the current IBASDTLM model first develops an entropy based weighting and first-order
cumulative moment (EWFCM) approach. Additionally, the DenseNet-121
technique was used as a module for extracting features. A BAS with an extreme
learning machine (ELM) model is used to classify the medical photos. A wide
variety of tests were carried out using a benchmark medical imaging dataset
to demonstrate the IBAS-DTL model’s noteworthy performance. The results
gained indicated the IBAS-DTL model’s superiority over its pre-existing
techniques.
Keywords
Cite This Article
M. I. Waly, "Intelligent beetle antenna search with deep transfer learning enabled medical image classification model,"
Computer Systems Science and Engineering, vol. 46, no.3, pp. 3159–3174, 2023. https://doi.org/10.32604/csse.2023.035900