Arithmetic Optimization Algorithm with Deep Learning-Based Medical X-Ray Image Classification Model

Abstract

AbstractRecently, number of medical X-ray images being generated is increasing rapidly due to the advancements in radiological equipment in medical centres. Medical X-ray image classification techniques are needed for effective decision making in the healthcare sector. Since the traditional image classification models are ineffective to accomplish maximum X-ray image classification performance, deep learning (DL) models have emerged. In this study, an Arithmetic Optimization Algorithm with Deep Learning-Based Medical X-Ray Image Classification (AOADL-MXIC) model has been developed. The proposed AOADL-MXIC model investigates the available X-ray images for the identification of diseases. Initially, the AOADL-MXIC model executes the pre-processing step using the Gabor filtering (GF) technique to eliminate the presence of noise. In the next level, the Capsule Network (CapsNet) model is utilized to derive feature vectors from the input X-ray images. Furthermore, for optimizing the hyperparameters related to the CapsNet approach, the AOA is exploited. Finally, the bidirectional gated recurrent unit (BiGRU) model is employed for the classification of medical X-ray images. The experimental result analysis of the AOADL-MXIC technique on a set of medical images stated the promising performance over the other models.KeywordsX-ray imagesArithmetic optimization algorithmDeep learningFeature extractionHyperparameter tuning