Intelligent Beetle Antenna Search with Deep Transfer Learning Enabled Medical Image Classification Model

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 techniquewas used as a module for extracting features.ABASwith 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. © 2023 CRL Publishing. All rights reserved.