Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture

Çınar, Ahmet; Yıldırım, Muhammed

doi:10.1016/j.mehy.2020.109684

Cited by 276 publications

(106 citation statements)

References 20 publications

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“…Deepak and Ameer [10] presented an identification technique using GoogLeNet and deep transfer learning for brain MRI images. Cinar and Yildirim [11] proposed a technique to diagnose the brain tumor using ResNet-50. In this model, the last five layers are removed and eight new layers are appended.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

Shukla

Sandhu

Ahirwar

et al. 2021

Mathematical Problems in Engineering

129

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COVID-19 is a new disease, caused by the novel coronavirus SARS-CoV-2, that was firstly delineated in humans in 2019.Coronaviruses cause a range of illness in patients varying from common cold to advanced respiratory syndromes such as Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Respiratory Syndrome (MERS-CoV). The SARS-CoV-2 outbreak has resulted in a global pandemic, and its transmission is increasing at a rapid rate. Diagnostic testing and approaches provide a valuable tool for doctors and support them with the screening process. Automatic COVID-19 identification in chest X-ray images can be useful to test for COVID-19 infection at a good speed. Therefore, in this paper, a framework is designed by using Convolutional Neural Networks (CNN) to diagnose COVID-19 patients using chest X-ray images. A pretrained GoogLeNet is utilized for implementing the transfer learning (i.e., by replacing some sets of final network CNN layers). 20-fold cross-validation is considered to overcome the overfitting quandary. Finally, the multiobjective genetic algorithm is considered to tune the hyperparameters of the proposed COVID-19 identification in chest X-ray images. Extensive experiments show that the proposed COVID-19 identification model obtains remarkably better results and may be utilized for real-time testing of patients.

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Section: Related Workmentioning

confidence: 99%

Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

Shukla

Sandhu

Ahirwar

et al. 2021

Mathematical Problems in Engineering

129

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“…In [118], [120], where the seed growing method was used for segmentation. The model was tested on 6 different BRATS datasets.…”

Section: ) Dl-based Approaches In Brain Tumor Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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“…In the improved hybrid model, the last five layers of Resnet50 have been removed. Ten new layers were added in place of these removed layers, and the number of layers increased from 177 to 182 [13]. The architecture of the proposed hybrid model is as in Figure 2.…”

Section: Structure Of Systemsmentioning

confidence: 99%

Classification of Alzheimer's Disease MRI Images with CNN Based Hybrid Method

Yıldırım¹,

Çınar²

2020

ISI

Self Cite

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Alzheimer is a type of dementia disease that is common in older ages. This disease is a progressive form of neurological disease that causes the destruction of brain cells. Since Alzheimer's is a progressive disease, various problems increase over time. For this reason, it is very important to diagnose the disease early and start the treatment process. In this study, it was tried to determine at which stage the disease is or whether it is Alzheimer using brain images. CNN architectures are used to diagnose the disease. In addition, a hybrid method we have developed has been proposed. With the architectures used, it is classified in 4 stages according to the disease progression level. In the proposed hybrid model, the Resnet50 method is used as the basis. The results are obtained separately by Alexnet, Resnet50, Densenet201, Vgg16, and the Hybrid method we developed. An accuracy of 90% has been achieved with the developed hybrid model. Consequently, when other scientific paper in the literature are investigated, it is finalized that the hybrid model developed to diagnose Alzheimer's disease has achieved the success achieved by other CNN architectures and even offers better results.

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Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture

Cited by 276 publications

References 20 publications

Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

Classification of Alzheimer's Disease MRI Images with CNN Based Hybrid Method

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