Explainable Cost-Sensitive Deep Neural Networks for Brain Tumor Detection from Brain MRI Images considering Data Imbalance

• URL: http://arxiv.org/abs/2308.00608v1
• Date: Tue, 1 Aug 2023 15:35:06 GMT
• Title: Explainable Cost-Sensitive Deep Neural Networks for Brain Tumor Detection from Brain MRI Images considering Data Imbalance
• Authors: Md Tanvir Rouf Shawon, G. M. Shahariar Shibli, Farzad Ahmed and Sajib Kumar Saha Joy
• Abstract summary: An automated pipeline is proposed, which encompasses five models: CNN, ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile. The performance of the proposed architecture is evaluated on a balanced dataset and found to yield an accuracy of 99.33% for fine-tuned InceptionV3 model. To further optimize the training process, a cost-sensitive neural network approach has been proposed in order to work with imbalanced datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents a research study on the use of Convolutional Neural Network (CNN), ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile models to efficiently detect brain tumors in order to reduce the time required for manual review of the report and create an automated system for classifying brain tumors. An automated pipeline is proposed, which encompasses five models: CNN, ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile. The performance of the proposed architecture is evaluated on a balanced dataset and found to yield an accuracy of 99.33% for fine-tuned InceptionV3 model. Furthermore, Explainable AI approaches are incorporated to visualize the model's latent behavior in order to understand its black box behavior. To further optimize the training process, a cost-sensitive neural network approach has been proposed in order to work with imbalanced datasets which has achieved almost 4% more accuracy than the conventional models used in our experiments. The cost-sensitive InceptionV3 (CS-InceptionV3) and CNN (CS-CNN) show a promising accuracy of 92.31% and a recall value of 1.00 respectively on an imbalanced dataset. The proposed models have shown great potential in improving tumor detection accuracy and must be further developed for application in practical solutions. We have provided the datasets and made our implementations publicly available at - https://github.com/shahariar-shibli/Explainable-Cost-Sensitive-Deep-Neural-Networks-for-Brain-Tumor- Detection-from-Brain-MRI-Images

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration [66.22668336495175]
  A lack of consideration for neural network calibration will not gain trust from humans. We introduce the Neural Clamping Toolkit, the first open-source framework designed to help developers employ state-of-the-art model-agnostic calibrated models.
  arXiv  Detail & Related papers  (2022-11-29T15:03:05Z)
• Lost Vibration Test Data Recovery Using Convolutional Neural Network: A Case Study [0.0]
  This paper proposes a CNN algorithm for the Alamosa Canyon Bridge as a real structure. Three different CNN models were considered to predict one and two malfunctioned sensors. The accuracy of the model was increased by adding a convolutional layer.
  arXiv  Detail & Related papers  (2022-04-11T23:24:03Z)
• Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network [1.7403133838762446]
  The incidences of atrial fibrillation (AFib) are increasing at a daunting rate worldwide. For the early detection of the risk of AFib, we have developed an automatic detection system based on deep neural networks. An end-to-end model is proposed to denoise the electrocardiogram signals using denoising autoencoders (DAE)
  arXiv  Detail & Related papers  (2022-01-26T21:45:48Z)
• CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded Systems [0.0]
  A Convolutional Neural Network (CNN) is a class of Deep Neural Network (DNN) widely used in the analysis of visual images captured by an image sensor. In this paper, we propose a neoteric variant of deep convolutional neural network architecture to ameliorate the performance of existing CNN architectures for real-time inference on embedded systems.
  arXiv  Detail & Related papers  (2021-12-01T18:20:52Z)
• Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State [66.2457134675891]
  Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. Most existing methods imitate the backpropagation framework and feedforward architectures for artificial neural networks. We propose a novel training method that does not rely on the exact reverse of the forward computation.
  arXiv  Detail & Related papers  (2021-09-29T07:46:54Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• Going deeper with brain morphometry using neural networks [18.851541271793085]
  Deep convolutional neural networks can infer morphometric measurements within a few seconds. We propose a more accurate and efficient neural network model for brain morphometry named HerstonNet.
  arXiv  Detail & Related papers  (2020-09-07T07:57:13Z)
• Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks [20.4701676109641]
  We present our system for the RSNA Intracranial Hemorrhage Detection challenge. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) We report a weighted mean log loss of 0.04989 on the final test set, which places us in the top 30 ranking (2%) from a total of 1345 participants.
  arXiv  Detail & Related papers  (2020-08-01T17:28:25Z)
• Neural Networks and Value at Risk [59.85784504799224]
  We perform Monte-Carlo simulations of asset returns for Value at Risk threshold estimation. Using equity markets and long term bonds as test assets, we investigate neural networks. We find our networks when fed with substantially less data to perform significantly worse.
  arXiv  Detail & Related papers  (2020-05-04T17:41:59Z)
• Widening and Squeezing: Towards Accurate and Efficient QNNs [125.172220129257]
  Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than that of networks with full-precision parameters. Most of existing methods aim to enhance performance of QNNs especially binary neural networks by exploiting more effective training techniques. We address this problem by projecting features in original full-precision networks to high-dimensional quantization features.
  arXiv  Detail & Related papers  (2020-02-03T04:11:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.