A Robust Brain Tumor Detector Using BiLSTM and Mayfly Optimization and Multi-Level Thresholding

Abstract

A brain tumor refers to an abnormal growth of cells in the brain that can be either benign or malignant. Oncologists typically use various methods such as blood or visual tests to detect brain tumors, but these approaches can be time-consuming, require additional human effort, and may not be effective in detecting small tumors. This work proposes an effective approach to brain tumor detection that combines segmentation and feature fusion. Segmentation is performed using the mayfly optimization algorithm with multilevel Kapur’s threshold technique to locate brain tumors in MRI scans. Key features are achieved from tumors employing Histogram of Oriented Gradients (HOG) and ResNet-V2, and a bidirectional long short-term memory (BiLSTM) network is used to classify tumors into three categories: pituitary, glioma, and meningioma. The suggested methodology is trained and tested on two datasets, Figshare and Harvard, achieving high accuracy, precision, recall, F1 score, and area under the curve (AUC). The results of a comparative analysis with existing DL and ML methods demonstrate that the proposed approach offers superior outcomes. This approach has the potential to improve brain tumor detection, particularly for small tumors, but further validation and testing are needed before clinical use.

Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion Technique for Rice Plant Disease Segmentation and Classification

“…where (ω 0 , ω 1 , …, ω (n-1) ) represents the probability of occurrence for n classes, and the MFO technique is used for the optimal threshold number. Similarly, the MFO method was projected for the mating process and the fighting feature of MFs [20]. The MFs in swarms can be identified as male and female individuals.…”

Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification

“…Furthermore, Papadomanolakis et al [3] presented a novel diagnostic framework based on convolutional neural networks (CNNs) and discrete wavelet transform (DWT) data analysis for glioma tumor diagnosis, showcasing impressive performance with potential clinical applications. Lastly, Mahum et al [6] proposed an effective approach that utilizes feature fusion, leveraging the mayfly optimization algorithm and multilevel thresholding for tumor localization. Their bidirectional long short-term memory (BiLSTM) network achieved remarkable results in classifying pituitary, glioma, and meningioma tumors.…”