A deep learning approach for brain tumor classification using MRI images

Aamir, Muhammad; Rahman, Ziaur; Dayo, Zaheer Ahmed; Abro, Waheed Ahmed; Uddin, M. Irfan; Khan, Inayat; Imran, Ali Shariq; Ali, Zafar; Ishfaq, Muhammad; Guan, Yurong; Hu, Zhihua

doi:10.1016/j.compeleceng.2022.108105

Cited by 118 publications

(39 citation statements)

References 25 publications

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“…In recent years, many studies have been carried out in the literature on brain tumor classification and brain tumor segmentation. Aamir et al, 12 used a different approach for brain tumor detection. In their study, they passed the dataset used in the first stage through a preprocessing process.…”

Section: Related Workmentioning

confidence: 99%

A novel convolutional neural network‐based approach for brain tumor classification using magnetic resonance images

Cinar

Kaya

2022

Int J Imaging Syst Tech

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Brain tumor is a disease that seriously threatens human health and can often be treated with risky surgeries. Experts detect brain tumor with high resolution magnetic resonance (MR) images. However, the expected accuracy value could not be reached in the studies carried out so far. The aim of this study is to develop a new approach for detecting brain tumor types using MR images. In the proposed approach, it is designed a CNN-based neural network from scratch. The results of the method were compared with existing networks. The proposed approach detected glioma tumors with 99.64%, meningiomas tumor with 96.53%, pituitary tumors with 98.39% and an average of 98.32% accuracy. The developed CNN based model is also compared with deep CNN models such as ResNet50, VGG19, DensetNet121 and InceptionV3, which are operated by transfer learning method. The results show that the proposed approach outperforms other deep neural networks.

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Section: Related Workmentioning

confidence: 99%

A novel convolutional neural network‐based approach for brain tumor classification using magnetic resonance images

Cinar

Kaya

2022

Int J Imaging Syst Tech

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“…In the medical field, the types of diseases can be identified according to medical images. For example, COVID-19 recognition can be performed by computer-aided methods using CT images [ 11 ], brain MRI images can be used to recognize brain tumors [ 12 , 13 ], and the types of breast cancer can be identified according to the histopathological images [ 14 ]. In later studies, SNNs were widely used in the fields of object recognition.…”

Section: Related Workmentioning

confidence: 99%

A Parallel Spiking Neural Network Based on Adaptive Lateral Inhibition Mechanism for Objective Recognition

Dong

2022

Computational Intelligence and Neuroscience

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Spiking neural network (SNN) has attracted extensive attention in the field of machine learning because of its biological interpretability and low power consumption. However, the accuracy of pattern recognition cannot completely surpass deep neural networks (DNNs). The main reason is that the inherent nondifferentiability of spiking neurons makes SNN unable to be trained directly by the gradient descent algorithm, and there is also no unified training algorithm for SNN. Inspired by the biological vision system, this paper proposes a parallel convolution SNN structure combined with an adaptive lateral inhibition mechanism. And, a way of dynamically evolving the time constant with the training of SNN is proposed to ensure the diversity of neurons. This paper verifies the effectiveness of the proposed methods on static datasets and neuromorphic datasets and extends it to the recognition of breast tumors. Experimental results show that the SNN has obvious advantages in dynamical datasets. For breast tumors, it is also an edge-based task, because the edge of a medical image contains the most important information in the image. This kind of information can provide great help for the noninvasive and accurate diagnosis of diseases. The Experimental results show that the proposed method is very close to the recognition results of DNNs on static datasets, and its performance on neuromorphic datasets exceeds that of DNNs.

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“…This is particularly important for an in vivo delineation tool, to fulfill the necessity of obtaining fastly accurate segmentation. Moreover, ML algorithms can improve the diagnostic value of the imaging modalities and speed up the analyses, for example, by helping in the detection of neurodegenerative diseases, 30 – 33 by allowing the detection and classification of multiple demyelinating diseases 34 , 35 or by enabling fast and accurate classification of tumors 36 – 40 from magnetic resonance imaging (MRI) data. Polarization imaged based ML frameworks were developed to accurately classify different tissue types 41 and neoplastic lesions in colon, 42 cervix, 43 and skin 44 .…”

Section: Introductionmentioning

confidence: 99%

Effects of formalin fixation on polarimetric properties of brain tissue: fresh or fixed?

et al. 2023

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.SignificanceImaging Mueller polarimetry (IMP) appears as a promising technique for real-time delineation of healthy and neoplastic tissue during neurosurgery. The training of machine learning algorithms used for the image post-processing requires large data sets typically derived from the measurements of formalin-fixed brain sections. However, the success of the transfer of such algorithms from fixed to fresh brain tissue depends on the degree of alterations of polarimetric properties induced by formalin fixation (FF).AimComprehensive studies were performed on the FF induced changes in fresh pig brain tissue polarimetric properties.ApproachPolarimetric properties of pig brain were assessed in 30 coronal thick sections before and after FF using a wide-field IMP system. The width of the uncertainty region between gray and white matter was also estimated.ResultsThe depolarization increased by 5% in gray matter and remained constant in white matter following FF, whereas the linear retardance decreased by 27% in gray matter and by 28% in white matter after FF. The visual contrast between gray and white matter and fiber tracking remained preserved after FF. Tissue shrinkage induced by FF did not have a significant effect on the uncertainty region width.ConclusionsSimilar polarimetric properties were observed in both fresh and fixed brain tissues, indicating a high potential for transfer learning.

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A deep learning approach for brain tumor classification using MRI images

Cited by 118 publications

References 25 publications

A novel convolutional neural network‐based approach for brain tumor classification using magnetic resonance images

A novel convolutional neural network‐based approach for brain tumor classification using magnetic resonance images

A Parallel Spiking Neural Network Based on Adaptive Lateral Inhibition Mechanism for Objective Recognition

Effects of formalin fixation on polarimetric properties of brain tissue: fresh or fixed?

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