Deep Fusion Model for Brain Tumor Classification Using Fine-Grained Gradient Preservation

• URL: http://arxiv.org/abs/2406.19690v1
• Date: Fri, 28 Jun 2024 07:06:02 GMT
• Title: Deep Fusion Model for Brain Tumor Classification Using Fine-Grained Gradient Preservation
• Authors: Niful Islam, Mohaiminul Islam Bhuiyan, Jarin Tasnim Raya, Nur Shazwani Kamarudin, Khan Md Hasib, M. F. Mridha, Dewan Md. Farid,
• Abstract summary: The research presents a novel architecture for precise brain tumor classification fusing pretrained ResNet152V2 and modified VGG16 models. The proposed solution incorporates various image processing techniques to improve image quality and achieves an astounding accuracy of 98.36% and 98.04% in Figshare and Kaggle datasets respectively.
• Score: 0.0807707808613597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain tumors are one of the most common diseases that lead to early death if not diagnosed at an early stage. Traditional diagnostic approaches are extremely time-consuming and prone to errors. In this context, computer vision-based approaches have emerged as an effective tool for accurate brain tumor classification. While some of the existing solutions demonstrate noteworthy accuracy, the models become infeasible to deploy in areas where computational resources are limited. This research addresses the need for accurate and fast classification of brain tumors with a priority of deploying the model in technologically underdeveloped regions. The research presents a novel architecture for precise brain tumor classification fusing pretrained ResNet152V2 and modified VGG16 models. The proposed architecture undergoes a diligent fine-tuning process that ensures fine gradients are preserved in deep neural networks, which are essential for effective brain tumor classification. The proposed solution incorporates various image processing techniques to improve image quality and achieves an astounding accuracy of 98.36% and 98.04% in Figshare and Kaggle datasets respectively. This architecture stands out for having a streamlined profile, with only 2.8 million trainable parameters. We have leveraged 8-bit quantization to produce a model of size 73.881 MB, significantly reducing it from the previous size of 289.45 MB, ensuring smooth deployment in edge devices even in resource-constrained areas. Additionally, the use of Grad-CAM improves the interpretability of the model, offering insightful information regarding its decision-making process. Owing to its high discriminative ability, this model can be a reliable option for accurate brain tumor classification.

Related papers

• Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation [0.0]
  Brain tumor segmentation is a critical task in medical image analysis, aiding in the diagnosis and treatment planning of brain tumor patients. Various deep learning-based techniques have made significant progress in this field, however, they still face limitations in terms of accuracy due to the complex and variable nature of brain tumor morphology. We propose a novel Hybrid Multihead Attentive U-Net architecture, to address the challenges in accurate brain tumor segmentation.
  arXiv  Detail & Related papers  (2024-05-22T02:46:26Z)
• A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation [0.0]
  Brain tumor is one of the severe health complications which lead to decrease in life expectancy of the individuals. Timely detection and prediction of brain tumors can be helpful to prevent death rates due to brain tumors. Deep learning-based approaches have emerged as a promising solution to develop automated biomedical image exploration tools.
  arXiv  Detail & Related papers  (2023-07-16T14:06:45Z)
• An Optimized Ensemble Deep Learning Model For Brain Tumor Classification [3.072340427031969]
  Inaccurate identification of brain tumors can significantly diminish life expectancy. This study introduces an innovative optimization-based deep ensemble approach employing transfer learning (TL) to efficiently classify brain tumors. 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.
  arXiv  Detail & Related papers  (2023-05-22T09:08:59Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• An Improved Deep Convolutional Neural Network by Using Hybrid Optimization Algorithms to Detect and Classify Brain Tumor Using Augmented MRI Images [0.9990687944474739]
  In this paper, an improvement in deep convolutional learning is ensured by adopting enhanced optimization algorithms. Experimental studies are conducted to validate the performance of the suggested method on a total number of 2073 augmented MRI images. The performance comparison shows that the DCNN-G-HHO is much more successful than existing methods, especially on a scoring accuracy of 97%.
  arXiv  Detail & Related papers  (2022-06-08T14:29:06Z)
• Brain Tumor Detection and Classification Using a New Evolutionary Convolutional Neural Network [18.497065020090062]
  The goal of this study is to employ brain MRI images to distinguish between healthy and unhealthy patients. Deep learning techniques have recently sparked interest as a means of diagnosing brain tumours more accurately and robustly.
  arXiv  Detail & Related papers  (2022-04-26T13:20:42Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
  We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
  arXiv  Detail & Related papers  (2020-02-03T22:25:01Z)

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.