Meta-learning based softmax average of convolutional neural networks using multi-layer perceptron for brain tumour classification

Abstract

Brain tumour classification using Magnetic Resonance Imaging (MRI) is crucial for medical decision-making. The variability in tumour shape, size, and position poses challenges to classification methods. Convolutional Neural Networks (CNNs) are commonly used due to their proven performance, but their effectiveness diminishes with the high variability of tumour characteristics. This study proposes a meta-learning approach, leveraging the softmax average of multiple CNN models with a Multi-Layer Perceptron (MLP) as the meta-learner. The base-learner models include MobileNetV2, InceptionV3, Xception, DenseNet201, and ResNet50. This approach combines the softmax outputs of these CNN models, capturing their strengths to handle diverse tumour characteristics. The averaged outputs are fed into the MLP for increased classification performance. To evaluate the proposed method, we used several brain MRI image datasets, including Dataset 1 (Thomas Dubail Dataset), Dataset 2 (Mesoud Nickparcar Dataset), and Dataset 3 (Fernando Feltrin Dataset). The test results showed the proposed method's effectiveness in improving classification performance. For Dataset 1, the MLP with one hidden layer (128 neurons) achieved 97.47 % accuracy, improving the base learners' performance by 1.94 %–7.42 %. On Dataset 2, the MLP with 64 neurons reached 99.54 % accuracy, with a 0 %–2.44 % improvement. For Dataset 3, an MLP with two hidden layers (256 and 125 neurons) achieved 98.87 % accuracy, enhancing performance by 0.46 %–5.67 %.