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Research on optimizing interpretability of EfficientNet architecture brain tumor classification model
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Xinru Guo, Weidong Lyu, Rui Wang, Dini Zhao
Electronic Measurement Technology | 2026, 49(6) : 220 - 228
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Electronic Measurement Technology | 2026, 49(6): 220-228
Information Technology and Image Processing
Research on optimizing interpretability of EfficientNet architecture brain tumor classification model
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Xinru Guo, Weidong Lyu, Rui Wang, Dini Zhao
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  • School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou 730070, China
doi: 10.19651/j.cnki.emt.2519530
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Brain tumors are highly invasive neurological diseases, and accurate early diagnosis is crucial for developing personalized treatment plans. Computer-aided diagnosis (CAD) based on deep learning techniques has achieved significant progress in medical image analysis, but limitations remain in terms of classification accuracy, computational efficiency, and interpretability. To address these issues, this study proposes an optimized EfficientNet model based on transfer learning and fine-tuning strategies. The model improves certain convolutional and fully connected layers and adds a global average pooling layer and a Dropout layer at the top of the network to enhance feature extraction capability and classification performance. Additionally, gradient-weighted class activation mapping (Grad-CAM) is introduced to visualize the model's decision-making process, effectively highlighting key discriminative regions of brain tumors, thereby improving interpretability and clinical reliability. Experimental results on the Figshare dataset demonstrate that the proposed model achieves an accuracy of 99.35% on the test set while significantly reducing parameter count and computational complexity, outperforming baseline models including VGG16, ResNet152V2, and Vision Transformer across all major metrics. Furthermore, cross-dataset validation shows that the model attains an accuracy of 92.51%, further demonstrating its robust stability and generalization capability.

brain tumor  /  EfficientNet  /  image classification  /  explainability
Xinru Guo, Weidong Lyu, Rui Wang, Dini Zhao. Research on optimizing interpretability of EfficientNet architecture brain tumor classification model[J]. Electronic Measurement Technology, 2026 , 49 (6) : 220 -228 . DOI: 10.19651/j.cnki.emt.2519530
Year 2026 volume 49 Issue 6
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doi: 10.19651/j.cnki.emt.2519530
  • Receive Date:2025-08-04
  • Online Date:2026-05-15
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  • Received:2025-08-04
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    School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou 730070, China
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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