JoWUA

Abstract

The study presents a new hybrid deep learning framework that combines the Multi-Function Differential Evolution (MF-DE) global optimization engine and the Morphological-OTSU analysis to improve the role of image segmentation and pattern recognition in this study, named as LungNet. The MF-DE engine has been utilized to optimize the hyperparameters of LSTM (Long Short-Term Memory). The morphological-OTSU method is adopted to perform effective image thresholding to help in the extraction of important features of images to enable proper classification. The proposed model is evaluated using the benchmark datasets, i.e., the LIDC-IDRI lung CT dataset in medical imaging and the NSCLC Radiogenomics dataset in lung cancer research. The MF-DE optimization ensured that the LSTM models were much more accurate, with the hyperparameters being optimized towards the optimal performance of the model. On LIDC-IDRI, the hybrid mode achieved a higher segmentation accuracy of 93.2% as compared to conventional methods that had an average segmentation accuracy of about 86.5%. Equally, the object recognition performance on the NSCLC Radiogenomics dataset achieved an mAP (mean Average Precision) of 48.0% that is 7.5% better than the baseline CNN models. The Hybrid Model had a classification accuracy on the LIDC-IDRI dataset of 95.0%, and on the NSCLC Radiogenomics dataset, the classification accuracy was 94.5%. This paper shows that the suggested hybrid design is better in global optimization and image analysis and provides significant improvements in real-time image processing tasks. The findings reveal a significant change in computational efficiency and accuracy of segmentation, especially with challenging and noisy data sets. The hybrid paradigm can be used in a very diverse range of applications, such as in medical image processing, industrial automation, and security surveillance systems.