A Comprehensive Study of Context Aware Hybrid Learning Approaches in Brain Tumor Segmentation

The brain tumor is a cancerous disease due to the growth of abnormal cells in the human brain, which causes the death of many precious lives because of lack of early identification and proper diagnosis. Nowadays, the brain tumor research gains more attention in the medical field, which leads to the development of medical systems with more advanced technology to compete for efficient and affordable solutions. But since brain tumors are highly heterogeneous in terms of location, shape, and size, developing automatic segmentation methods has remained a challenging task over decades. Computer-aided diagnosis (CAD) approaches help detect, classify, and grade tumors from medical images like MRIs and CT scans. This paper presents a comprehensive study on brain tumor MRI image segmentation, covering commonly used methods, databases and performance metrics. The methods are categorized into conventional, machine learning-based, deep learning-based, and hybrid approaches. This paper reviews the evolution of automated brain tumor segmentation models using multi modal MR images, focusing on the BraTs 2020, studying changing trends and key parameters affecting performance. This study also provides a comprehensive of brain tumor segmentation methods, highlighting the effectiveness of context aware hybrid deep learning approaches, notably CapsNet_and_VGGNet, which achieved a high accuracy of 99.67%. Further, this paper summarizes principles, structures, advantages and disadvantages of typical algorithms and examines challenges and future trends.