Meta-learning based softmax average of convolutional neural networks using multi-layer perceptron for brain tumour classification
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...
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Elsevier
2025-07-01
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| author | Irwan Budi Santoso Shoffin Nahwa Utama Supriyono |
| author_facet | Irwan Budi Santoso Shoffin Nahwa Utama Supriyono |
| author_sort | Irwan Budi Santoso |
| collection | DOAJ |
| description | 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 %. |
| format | Article |
| id | doaj-art-539d63477ce7472ca079efc4f4842637 |
| institution | Kabale University |
| issn | 2590-0056 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
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| spelling | doaj-art-539d63477ce7472ca079efc4f48426372025-08-20T03:31:11ZengElsevierArray2590-00562025-07-012610039810.1016/j.array.2025.100398Meta-learning based softmax average of convolutional neural networks using multi-layer perceptron for brain tumour classificationIrwan Budi Santoso0Shoffin Nahwa Utama1 Supriyono2Corresponding author.; Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Maulana Malik Ibrahim, Malang, IndonesiaInformatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Maulana Malik Ibrahim, Malang, IndonesiaInformatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Maulana Malik Ibrahim, Malang, IndonesiaBrain 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 %.http://www.sciencedirect.com/science/article/pii/S2590005625000256Brain tumourMagnetic resonance imagingConvolutional neural networkMulti-layer perceptronSoftmaxBase-learner |
| spellingShingle | Irwan Budi Santoso Shoffin Nahwa Utama Supriyono Meta-learning based softmax average of convolutional neural networks using multi-layer perceptron for brain tumour classification Array Brain tumour Magnetic resonance imaging Convolutional neural network Multi-layer perceptron Softmax Base-learner |
| title | Meta-learning based softmax average of convolutional neural networks using multi-layer perceptron for brain tumour classification |
| title_full | Meta-learning based softmax average of convolutional neural networks using multi-layer perceptron for brain tumour classification |
| title_fullStr | Meta-learning based softmax average of convolutional neural networks using multi-layer perceptron for brain tumour classification |
| title_full_unstemmed | Meta-learning based softmax average of convolutional neural networks using multi-layer perceptron for brain tumour classification |
| title_short | Meta-learning based softmax average of convolutional neural networks using multi-layer perceptron for brain tumour classification |
| title_sort | meta learning based softmax average of convolutional neural networks using multi layer perceptron for brain tumour classification |
| topic | Brain tumour Magnetic resonance imaging Convolutional neural network Multi-layer perceptron Softmax Base-learner |
| url | http://www.sciencedirect.com/science/article/pii/S2590005625000256 |
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