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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.


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.


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.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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