Few-Shot Learning for Brain Tumour Classification

Brain tumours are abnormal and uncontrollable multiplication of cells in the brain. They can have significant implications for patients, ranging from headaches and nausea to more severe symptoms such as seizures and memory difficulties. An accurate diagnosis is vital to implement a successful treatment plan. Current deep learning approaches for classifying brain tumours rely on the availability of large amounts of labelled data. This can be challenging, as highly trained medical professionals need to manually label each image. To address these limitations, this work proposes a few-shot learning method that uses a varying number of shots, ranging from 1-shot to 10-shots. Three categories of brain tumours are classified: glioma, meningioma, and pituitary tumours. The proposed approach demonstrates 96.30 % and 96.96 % accuracies for the 1-shot and 10-shot models, respectively. The state-of-the-art CNN method achieved a 97.07% accuracy. However, this approach required significantly larger quantities of labelled data. This development could improve the classification of brain tumours, particularly for rare diseases or instances where there is a scarcity of labelled data.