mlsanityData to 14 Oct 2026

An Optimized Ensemble Deep Learning Model For Brain Tumor Classification

arXiv 2305.12844 ↗First posted 22 May 2023eess.IV cs.CV cs.LGcited by 4

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Abstract (arXiv)

Brain tumors present a grave risk to human life, demanding precise and timely diagnosis for effective treatment. Inaccurate identification of brain tumors can significantly diminish life expectancy, underscoring the critical need for precise diagnostic methods. Manual identification of brain tumors within vast Magnetic Resonance Imaging (MRI) image datasets is arduous and time-consuming. Thus, the development of a reliable deep learning (DL) model is essential to enhance diagnostic accuracy and ultimately save lives. This study introduces an innovative optimization-based deep ensemble approach employing transfer learning (TL) to efficiently classify brain tumors. Our methodology includes meticulous preprocessing, reconstruction of TL architectures, fine-tuning, and ensemble DL models utilizing weighted optimization techniques such as Genetic Algorithm-based Weight Optimization (GAWO) and Grid Search-based Weight Optimization (GSWO). Experimentation is conducted on the Figshare Contrast-Enhanced MRI (CE-MRI) brain tumor dataset, comprising 3064 images. Our approach achieves notable accuracy scores, with Xception, ResNet50V2, ResNet152V2, InceptionResNetV2, GAWO, and GSWO attaining 99.42%, 98.37%, 98.22%, 98.26%, 99.71%, and 99.76% accuracy, respectively. Notably, GSWO demonstrates superior accuracy, averaging 99.76% accuracy across five folds on the Figshare CE-MRI brain tumor dataset. The comparative analysis highlights the significant performance enhancement of our proposed model over existing counterparts. In conclusion, our optimized deep ensemble model exhibits exceptional accuracy in swiftly classifying brain tumors. Furthermore, it has the potential to assist neurologists and clinicians in making accurate and immediate diagnostic decisions.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 5 of its 63 references that are in the map. First posted on arXiv 22 May 2023.

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5 of 63 references.

References · 5

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  3. 2016Identity Mappings in Deep Residual Networkscited by 1,965
  4. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
  5. 2022Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learningcited by 13

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Cited by year

Citing papers per year: 2023: 2; 2024: 2.

4 papers.

  1. 2024Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extractioncited by 16
  2. 2023Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecturecited by 8
  3. 2023A Novel Method for improving accuracy in neural network by reinstating traditional back propagation techniquecited by 0
  4. 2024Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scanscited by 0