• T. Kumar
• R. Ponnusamy

Medical X-ray image classification is task where machine learning (ML) approaches are utilized to categorize X-ray images into various categories on the basis of some criteria. The core objective is to automate the task of evaluating X-ray images and enhance the efficiency and accuracy of diagnosis. Current advancements in deep learning (DL) have given rise to better performance in various medical image analysis processes. Chest radiographs the frequently performed radiological exam, are specifically significant modalities for which various applications were studied. The issue of publicly available, multiple, large, chest X-ray datasets in recent times has increased investigation. Therefore, this article presents a new barnacles mating optimizer with deep learning based medical image classification model (BMODL-MICM) for X-ray images. The presented BMODL-MICM technique uses Wiener filtering (WF) as a preprocessing step to eradicate the noise. Besides, ShuffleNetv2 feature extractor is involved to create a collection of feature vectors. Moreover, the BMODL-MICM technique uses BMO algorithm for hyperparameter tuning procedure. At last, Elman Neural Network (ENN) technique is applied for the classification of X-ray images. The experimental assessment of the BMODL-MICM approach on benchmark X-ray image dataset reported its effectual outcome over the other methods.

Medical image classification; X-ray images; Barnacles mating optimizer; Deep learning; Chest radiographs

"Design of Sparrow Search Algorithm with Deep Transfer Learning-based Classification Model for Medical X-Ray Image Classification Mode", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.d690-d702, November-2023, Available :http://www.jetir.org/papers/JETIR2311385.pdf

"Design of Sparrow Search Algorithm with Deep Transfer Learning-based Classification Model for Medical X-Ray Image Classification Mode", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppd690-d702, November-2023, Available at : http://www.jetir.org/papers/JETIR2311385.pdf