Multiclass deep learning model for predicting lung diseases based on honey badger algorithm

This paper presents a deep learning (DL) model called deep Honey Badger Optimization Algorithm (HBOA), which uses metaheuristic optimization techniques to predict lung diseases from chest X-ray images. A chest X-ray is a simple, popular, and affordable diagnostic imaging technique. However, the technique’s use is severely constrained due to a significant shortage of experienced radiologists. The model uses the Honey Badger Optimization Algorithm to optimize the DL hyperparameters. Experiments on 10,000 X-ray images from four lung disease classes showed a 99.55% classification accuracy, outperforming related work in testing accuracy, loss (0.0382), F1-score (99.16%), and AUC (99.77%). This promising performance measurement demonstrates the model’s potential for lung disease diagnosis.