Brain Tumor Classification Using ResNet-101 Based Squeeze and Excitation Deep Neural Network

Ghosal, Palash; Nandanwar, Lokesh; Kanchan, Swati; Bhadra, Ashok Kumar; Chakraborty, Jayasree; Nandi, Debashis

doi:10.1109/icaccp.2019.8882973

Cited by 104 publications

(54 citation statements)

References 12 publications

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“…Since the performance of ML classifiers are highly dependent on input feature map, designing a method to produce a discriminative and informative feature from brain MR images plays a key role to successfully build the model for MRI-based brain tumor classification. In recent years, several studies proposed deep-learning-based feature extraction methods for MRI-based brain tumor classification using pre-trained deep CNN models: ResNet-50 [ 69 , 70 ], ResNet-101 [ 71 ], DenseNet-121 [ 70 , 72 ], VGG-16 [ 69 , 70 ], VGG-19 [ 70 , 73 ], AlexNet [ 74 ], Inception V1 (GoogLeNet) [ 29 ], Inception V3 [ 69 , 75 ], and MobileNet V2 [ 76 ]. However, no study has been carried out to evaluate the effectiveness of several pre-trained deep CNN models as a feature extractor for MRI-based brain tumor classification task.…”

Section: Proposed Methodsmentioning

confidence: 99%

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Kang

Ullah

Gwak

2021

Sensors

346

140

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Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.

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Section: Proposed Methodsmentioning

confidence: 99%

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Kang

Ullah

Gwak

2021

Sensors

346

140

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“…In other words, by stacking residual layers, significantly deeper architectures could be designed. For the case of this research, three different architectures based on the residual block will be used, and these are: ResNet50 [ 69 ], ResNet101 [ 70 ], and ResNet152 [ 71 ]. The aforementioned architectures are pre-defined ResNet architectures that are mainly used for image recognition and computer vision problems which require deeper CNN configurations.…”

Section: Description Of Used Convolutional Neural Networkmentioning

confidence: 99%

Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

Lorencin

Šegota

Anđelić

et al. 2021

JPM

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COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.

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“…The models used in our experiments are the ResNet18, ResNet50, and ResNet101 (Alom et al, 2018;Ghosal et al, 2019;Wu et al, 2019).…”

Section: State-of-the-art Cnns For Transfer Learningmentioning

confidence: 99%

Deep convolutional neural networks for COVID‐19 automatic diagnosis

Emara

Shoaib

Elwekeil

et al. 2021

Microscopy Res & Technique

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This article is mainly concerned with COVID-19 diagnosis from X-ray images. The number of cases infected with COVID-19 is increasing daily, and there is a limitation in the number of test kits needed in hospitals. Therefore, there is an imperative need to implement an efficient automatic diagnosis system to alleviate COVID-19 spreading among people. This article presents a discussion of the utilization of convolutional neural network (CNN) models with different learning strategies for automatic COVID-19 diagnosis. First, we consider the CNN-based transfer learning approach for automatic diagnosis of COVID-19 from X-ray images with different training and testing ratios. Different pre-trained deep learning models in addition to a transfer learning model are considered and compared for the task of COVID-19 detection from X-ray images. Confusion matrices of these studied models are presented and analyzed. Considering the performance results obtained, ResNet models (ResNet18, ResNet50, and ResNet101) provide the highest classification accuracy on the two considered datasets with different training and testing ratios, namely 80/20, 70/30, 60/40, and 50/50. The accuracies obtained using the first dataset with 70/30 training and testing ratio are 97.67%, 98.81%, and 100% for ResNet18, ResNet50, and ResNet101, respectively. For the second dataset, the reported accuracies are 99%, 99.12%, and 99.29% for ResNet18, ResNet50, and ResNet101, respectively. The second approach is the training of a proposed CNN model from scratch. The results confirm that training of the CNN from scratch can lead to the identification of the signs of COVID-19 disease.

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Brain Tumor Classification Using ResNet-101 Based Squeeze and Excitation Deep Neural Network

Cited by 104 publications

References 12 publications

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

Deep convolutional neural networks for COVID‐19 automatic diagnosis

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