Enhanced medical image segmentation using optimized bidirectional LSTM and dolphin partner optimizer

Medical imaging has become a standard in diagnosing and treating how organs and tissues operate. In earlier systems, machine learning approaches were used for segmentation over a long period. Thus, processing and analyzing these medical images are highly important for clinical diagnosis. Deep learning-based image segmentation has attracted a lot of interest in recent years. This paper introduces the Optimized Bidirectional Long-Short Term Memory (OBi-LSTM) segmented technique for medical image classification. The OBi-LSTM classifier contains the sequencing data in both directions, backward and forward. It is utilized to simultaneously be aware of the most significant spatial placements, channels, and scales. Using Dolphin Partner Optimizer (DPO), the weight and bias parameters of the Bi-LSTM classifier are tuned for each cell. Since these variables are shared across the entire sequence, the network can maintain effective representation while requiring fewer unique weight parameters and hidden neurons, reducing overall computational complexity. OBi-LSTM classification outperforms state-of-the-art techniques in the MRI segmented test, demonstrating the proposed approach's usefulness and strong explanatory power. The proposed OBi-LSTM model achieves a high dice similarity coefficient of 94.05%, a Jaccard resemblance index of 88.77%, and an accuracy ratio of 93.05% compared to other existing models. The proposed OBi-LSTM + DPO model significantly enhances medical image segmentation performance, particularly in boundary delineation and feature extraction.