Intelligent Ultra-Light Deep Learning Model for Multi-Class Brain Tumor Detection

Abstract

The diagnosis and surgical resection using Magnetic Resonance (MR) images in brain tumors is a challenging task to minimize the neurological defects after surgery owing to the non-linear nature of the size, shape, and textural variation. Radiologists, clinical experts, and brain surgeons examine brain MRI scans using the available methods, which are tedious, error-prone, time-consuming, and still exhibit positional accuracy up to 2–3 mm, which is very high in the case of brain cells. In this context, we propose an automated Ultra-Light Brain Tumor Detection (UL-BTD) system based on a novel Ultra-Light Deep Learning Architecture (UL-DLA) for deep features, integrated with highly distinctive textural features, extracted by Gray Level Co-occurrence Matrix (GLCM). It forms a Hybrid Feature Space (HFS), which is used for tumor detection using Support Vector Machine (SVM), culminating in high prediction accuracy and optimum false negatives with limited network size to fit within the average GPU resources of a modern PC system. The objective of this study is to categorize multi-class publicly available MRI brain tumor datasets with a minimum time thus real-time tumor detection can be carried out without compromising accuracy. Our proposed framework includes a sensitivity analysis of image size, One-versus-All and One-versus-One coding schemes with stringent efforts to assess the complexity and reliability performance of the proposed system with K-fold cross-validation as a part of the evaluation protocol. The best generalization achieved using SVM has an average detection rate of 99.23% (99.18%, 98.86%, and 99.67%), and F-measure of 0.99 (0.99, 0.98, and 0.99) for (glioma, meningioma, and pituitary tumors), respectively. Our results have been found to improve the state-of-the-art (97.30%) by 2%, indicating that the system exhibits capability for translation in modern hospitals during real-time surgical brain applications. The method needs 11.69 ms with an accuracy of 99.23% compared to 15 ms achieved by the state-of-the-art to earlier to detect tumors on a test image without any dedicated hardware providing a route for a desktop application in brain surgery.

Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification

“…Moorthi and Agita [16] suggested a fresh technique termed Level Set-related Back Propagation Neural Network (LS-BPNN) for the mechanical classification and recognition of liver cancer. In [17][18][19][20], the researchers enhanced a DL oriented assistant for helping diagnosticians distinguish between 2 sub-kinds of fundamental liver cancer, cholangiocarcinoma and hepatocellular carcinoma, on eosin and hematoxylin stained whole slide images (WSI) and assessed its impact on the diagnostic outcomes of eleven diagnosticians with changing stages of skills.…”

Ensemble deep learning for brain tumor detection

“…According to the literature review, numerous machine learning techniques have been employed to categorize MRI images (Rehman et al, 2020;Zhou et al, 2020;Kumar et al, 2021). Recent advances in machine learning have led to the application of numerous deep learning approaches for diagnosing MRI images (Alanazi et al, 2022;Alrashedy et al, 2022;Qureshi et al, 2022;Senan et al, 2022;Zeineldin et al, 2022). The main contributions of the research are given below:…”

“…The automated Ultra-Light Brain Tumor Detection (UL-BTD) system is proposed in Qureshi et al (2022) that is based on the new Ultra-Light Learning Architecture (UL-DLA) for the in-depth features, merged with the textural features that extracted from Gray Level Co-occurrence Matrix (GLCM). It created the Hybrid Feature Space (HFC) for detecting the brain tumor using a support vector machine.…”

Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features

“…The GLCM-based texture features were composed of contrast, sum of square variance, cluster shade [36], correlation [37], and two values of homogeneity [37][38][39]. GLCM features have been successfully utilized in the classification of breast tissues [40], and many other medical imaging problems [36,[41][42][43] detailed in [44][45][46].…”