A Comprehensive Analysis of Brain Tumor Segmentation and Classification using Artificial Intelligence Techniques

A Brain Tumor (BT) is the uncontrolled growth of cancerous cells that affect parts of the brain and have significant impacts on the body. Identifying BT is challenging, and early detection is difficult due to symptoms such as seizures and vision problems, which are often unclear and can lead to inaccurate diagnoses. To overcome these challenges, Artificial Intelligence (AI) techniques, such as Machine Learning (ML) and Deep Learning (DL) methods, are utilized for segmenting and classifying BT. ML techniques, such as Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT), along with DL techniques like Bi-directional Long Short-Term Memory (Bi-LSTM), Deep Belief Network (DBN), and Hybrid Convolutional Neural Network (CNN) architectures, have been applied in segmentation and classification approaches. This survey on BT segmentation and classification explores the current techniques in diagnosis, with advanced treatments aimed at providing accurate analysis of BT. Evaluation metrics such as sensitivity, peak signal-to-noise ratio, accuracy, precision, and specificity are used to assess the performance of these techniques.