Few-Shot Classification Of Brain Cancer Images Using Meta-Learning Algorithms

The primary objective of deep learning is to have good performance on a large dataset. However, when the model lacks sufficient data, it becomes a challenge to achieve high accuracy in predicting these unfamiliar classes. In fact, the real-world dataset often introduces new classes, and some types of data are difficult to collect or simulate, such as medical images. A subset of machine learning is meta learning, or "learning-to-learn", which can tackle these problems. In this paper, a few-shot classification model is proposed to classify three types of brain cancer: Glioma brain cancer, Meningioma brain cancer, and brain Tumor cancer. To achieve this, we employ an episodic meta-training paradigm that integrates the model-agnostic meta-learning (MAML) framework with a prototypical network (ProtoNet) to train the model. In detail, ProtoNet focuses on learning a metric space by computing distances to class prototypes of each class, while MAML concentrates on finding the optimal initialization parameters for the model to enable the model to learn quickly on a few labeled samples. In addition, we compute and report the average accuracy for the baseline and our methods to assess the quality of the prediction confidence. Simulation results indicate that our proposed approach substantially surpasses the performance of the baseline ResNet18 model, achieving an average accuracy improvement from 46.33% to 92.08% across different few-shot settings. These findings highlight the potential of combining metric-based and optimization-based meta-learning techniques to improve diagnostic support in healthcare applications.