Brain Tumor Classification Using Medial Residual Encoder Layers

• URL: http://arxiv.org/abs/2011.00628v2
• Date: Thu, 9 Dec 2021 05:59:06 GMT
• Title: Brain Tumor Classification Using Medial Residual Encoder Layers
• Authors: Zahra SobhaniNia, Nader Karimi, Pejman Khadivi, Roshank Roshandel, Shadrokh Samavi
• Abstract summary: Cancer is the second leading cause of death worldwide, responsible for over 9.5 million deaths in 2018 alone. Brain tumors count for one out of every four cancer deaths. We propose a system based on deep learning, containing encoder blocks. These blocks are fed with post-max-pooling features as residual learning. Experimental evaluations of this model on a dataset consisting of 3064 MR images show 95.98% accuracy, which is better than previous studies on this database.
• Score: 9.038707616951795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: According to the World Health Organization (WHO), cancer is the second leading cause of death worldwide, responsible for over 9.5 million deaths in 2018 alone. Brain tumors count for one out of every four cancer deaths. Therefore, accurate and timely diagnosis of brain tumors will lead to more effective treatments. Physicians classify brain tumors only with biopsy operation by brain surgery, and after diagnosing the type of tumor, a treatment plan is considered for the patient. Automatic systems based on machine learning algorithms can allow physicians to diagnose brain tumors with noninvasive measures. To date, several image classification approaches have been proposed to aid diagnosis and treatment. For brain tumor classification in this work, we offer a system based on deep learning, containing encoder blocks. These blocks are fed with post-max-pooling features as residual learning. Our approach shows promising results by improving the tumor classification accuracy in Magnetic resonance imaging (MRI) images using a limited medical image dataset. Experimental evaluations of this model on a dataset consisting of 3064 MR images show 95.98% accuracy, which is better than previous studies on this database.

Related papers

• Brain Tumor Classification From MRI Images Using Machine Learning [0.24739484546803336]
  Brain tumor is a life-threatening problem and hampers the normal functioning of the human body. The use of deep learning algorithms in medical imaging has resulted in significant improvements in the classification and diagnosis of brain tumors. The objective of this project is to develop a predictive system for brain tumor detection using machine learning.
  arXiv  Detail & Related papers  (2024-07-15T11:30:40Z)
• Comparative Evaluation of Transfer Learning for Classification of Brain Tumor Using MRI [0.5235143203977018]
  Brain cancer diagnosis has been considerably expedited by the field of computer-assisted diagnostics. In our study, we categorize three different kinds of brain tumors using four transfer learning techniques. Our models were tested on a benchmark dataset of $3064$ MRI pictures representing three different forms of brain cancer.
  arXiv  Detail & Related papers  (2023-09-24T03:46:38Z)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning [55.789874096142285]
  We present a deep learning-based brain tumor recurrence location prediction network. We first train a multi-modal brain tumor segmentation network on the public dataset BraTS 2021. Then, the pre-trained encoder is transferred to our private dataset for extracting the rich semantic features. Two decoders are constructed to jointly segment the present brain tumor and predict its future tumor recurrence location.
  arXiv  Detail & Related papers  (2023-04-11T02:45:38Z)
• CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans [45.83431075462771]
  Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice. Most medical AI systems are built to focus on single organs with a narrow list of a few diseases. CancerUniT is a query-based Mask Transformer model with the output of multi-tumor prediction.
  arXiv  Detail & Related papers  (2023-01-28T20:09:34Z)
• 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)
• Triplet Contrastive Learning for Brain Tumor Classification [99.07846518148494]
  We present a novel approach of directly learning deep embeddings for brain tumor types, which can be used for downstream tasks such as classification. We evaluate our method on an extensive brain tumor dataset which consists of 27 different tumor classes, out of which 13 are defined as rare.
  arXiv  Detail & Related papers  (2021-08-08T11:26:34Z)
• Brain Tumors Classification for MR images based on Attention Guided Deep Learning Model [3.6328238032703806]
  We analyze the existing technology and propose an attention guided deep convolution neural network (CNN) model. Our method can achieve the average accuracy of 99.18% under ten-fold cross-validation for identifying the presence or absence of tumor. It can assist doctors in achieving efficient clinical diagnosis of brain tumors.
  arXiv  Detail & Related papers  (2021-04-06T07:25:52Z)
• Deep Convolutional Neural Networks Model-based Brain Tumor Detection in Brain MRI Images [0.0]
  Our work involves implementing a deep convolutional neural network (DCNN) for diagnosing brain tumors from MR images. Our model can single out the MR images with tumors with an overall accuracy of 96%.
  arXiv  Detail & Related papers  (2020-10-03T07:42:17Z)
• Multi-channel MRI Embedding: An EffectiveStrategy for Enhancement of Human Brain WholeTumor Segmentation [2.869946954477617]
  One of the most important tasks in medical image processing is the brain's whole tumor segmentation. Brain tumors often can be malignant or benign, if they are detected at an early stage. Our research introduces an efficient strategy called Multi-channel MRI embedding to improve the result of deep learning-based tumor segmentation.
  arXiv  Detail & Related papers  (2020-09-13T23:44:16Z)
• 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.