A Review on Brain Tumour Detection and Classification Using Deep Learning Techniques

Successful treatment and better patient outcomes depend on early identification and correct diagnosis of brain tumours. The study looks at how deep learning may help detect brain tumours, including how convolutional neural networks (CNNs) can change the face of diagnostic imaging. It emphasizes the significance of accurate and early detection of brain cancer for effective treatment and improved patient outcomes. The study discusses the challenges associated with tumour segmentation and classification, such as variations in size, shape, and location. To overcome these challenges, deep learning techniques are utilized to analyse brain scans and identify tumour abnormalities. However, the paper underscores the importance of these models serving as tools to assist medical professionals rather than replacing their expertise. Additionally, the paper presents a comparative study that evaluates different techniques, artificial neural networks (ANN) achieve the highest accuracy at 99%, followed by Support Vector Machine (SVM) at 98.9%. vision transformer (ViT), Extreme Gradient Boosting (XG Boost), CNN-based dense Efficient Net, and Deep Neural Network (DNN) also show high accuracies above 98%. However, U-Net, Alex Net, hybrid ensemble method (Random Forest (RF), K-Nearest Neighbour (KNN), Decision Tree (DT), and Region-based convolutional neural networks (R-CNN)) have lower accuracies ranging from 91.66% to 97.305%. Overall, the findings highlight the potential of deep learning in enhancing the efficiency and precision of brain tumour diagnosis, leading to earlier treatment and improved patient outcomes.