Hybrid similarity measure-based image indexing and Gradient Ladybug Beetle optimization for retrieval of brain tumor using MRI

• Published in: The Journal of supercomputing Vol. 80; no. 16; pp. 24051 - 24078
• Main Authors: Sudhish, Dhanya K., Nair, Latha R., Sivan, Shailesh
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2024; Springer Nature B.V
• Subjects: Accuracy; Artificial neural networks; Beetles; Brain; Brain cancer; Classification; Compilers; Computer Science; Datasets; Discrete Wavelet Transform; Feature extraction; Glioma; Image retrieval; Image segmentation; Interpreters; Literature reviews; Magnetic resonance imaging; Medical imaging; Neural networks; Neurology; NMR; Nuclear magnetic resonance; Optimization; Optimization algorithms; Processor Architectures; Programming Languages; Retrieval; Similarity; Similarity measures; Tumors; Wavelet transforms; Brain tumor (BT); LeNet; Gradient descent (GD); 3D-convolutional neural network (CNN); Image retrieval; Ladybug Beetle Optimization (LBO)
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-024-06350-z

Clinical images of brain tumors (BT) are crucial in the diagnostic process and contain substantial medical information. In neurosurgery and neurology, AI’s application in retrieving and analyzing brain tumors leads to earlier, more accurate diagnoses and improves treatment planning. However, the accuracy of the existing methods for the physical retrieval of similar images needs to be improved. This paper introduces Gradient Ladybug Beetle Optimization-based LeNet (GLBO-LeNet) for the retrieval of brain tumor magnetic resonance images (MRI) from the medical datasets. This approach processes both input MRI images and query MRIs using the same pipeline. Tumor segmentation process is performed on these images using a 3D Convolutional Neural Network (CNN). Features are extracted from segmented images, incorporating a novel feature extraction method, LTDP based on Discrete Wavelet Transform (DWT) with Pyramid Histogram of Orientation (PHoG). The extracted features are utilized for tumor classification using LeNet-5, tuned by Gradient Ladybug Beetle Optimization (GLBO). The classified outputs from input MRI images are indexed in an image database. Similar images are retrieved and ranked using a proposed hybrid similarity measure, enabling efficient brain MRI image retrieval. In this study, the GLBO-LeNet-based brain tumor MRI retrieval system achieved an accuracy of 91.5%, a Prue-positive rate (TPR) of 91.9%, a True-negative rate (TNR) of 92.5%, a Positive predictive value (PPV) of 90.8% and a Negative predictive value (NPV) of 89.4%.