Tunicate swarm-based grey wolf algorithm for fetal heart chamber segmentation and classification: a heuristic-based optimal feature selection concept

• Published in: Journal of intelligent & fuzzy systems 2023-01, Vol.44 (1), p.1029-1041
• Main Authors: Shobana Nageswari, C., Vimal Kumar, M.N., Vini Antony Grace, N., Thiyagarajan, J.
• Format: Article
• Language: English
• Subjects: Algorithms; Classification; Feature extraction; Feature selection; Image classification; Image quality; Image segmentation; Medical imaging; Medical research; Optimization; Quality assessment; Quality management; Ultrasonic imaging
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-221654

Ultrasound image quality management and assessment are an important stage in clinical diagnosis. This operation is often carried out manually, which has several issues, including reliance on the operator’s experience, lengthy labor, and considerable intra-observer variance. As a result, automatic quality evaluation of Ultrasound images is particularly desirable in medical applications. This research work plans to perform the fetal heart chamber segmentation and classification using the novel intelligent technology named as hybrid optimization algorithm Tunicate Swarm-based Grey Wolf Algorithm (TS-GWA). Initially, the US fetal images data is collected and data undergoes the preprocessing using the total variation technique. From the preprocessed images, the optimal features are extracted using the TF-IDF approach. Then, Segmentation is processed on optimally selected features using Spatially Regularized Discriminative Correlation Filters (SRDCF) method. In the final step, the classification of fetal images is done using the Modified Long Short-Term Memory (MLSTM) Network. The fitness function behind the optimal feature selection as well as the hidden neuron optimization of MLSTM is the maximization of PSNR and minimization of MSE. The PSNR value is improved from 3.1 to 9.8 in the proposed method and accuracy of the proposed classification algorithm is improved from 1.9 to 12.13 compared to other existing techniques. The generalization ability and the adaptability of proposed TS-GWA method are described by conducting the various performance analysis. Extensive performance result shows that proposed intelligent techniques performs better than the existing segmentation methods.