A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images

Pang, Shuchao; Yu, Zhezhou; Orgun, Mehmet A.

doi:10.1016/j.cmpb.2016.12.019

Cited by 76 publications

(34 citation statements)

References 17 publications

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“…The purpose of medical imaging is to aid radiologists and clinicians to make the diagnostic and treatment process more efficient. Medical imaging is a predominant part of diagnosis and treatment of diseases and represents different imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound, X-ray and hybrid modalities [7], [15]. These modali-ties play a vital role in the detection of anatomical and functional information about different body organs for diagnosis as well as research.…”

Section: Methodsmentioning

confidence: 99%

“…The design is applied for various CT scan images of lungs, MR images of brain, X-ray images, etc. Another example, we see the work of Shuchao Pang et al in [7]; they proposed a computational model for image classification based on end-to-end classifier using domain transferred deep convolutional neural networks or CNN. The proposed domain transferred deep CNN (DT-DCNN) showed significant increase in the accuracy while classifying the medical images with high precision.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparative study of performing features applied in CNN architectures

Laroui¹,

Lazaar²,

Mahboub³

2019

Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 20

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Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including Medical image analysis. CNN is designed to automatically and adaptively learn spatial hierarchies of features through back-propagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This paper presents an approach based on CNN for the classification of brain tumors, based on several characteristics that will be extracted automatically and some performing features that will be used in our CNN, This proposed approach provides efficient results at the level of automatic classification than the other usual methods.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative study of performing features applied in CNN architectures

Laroui¹,

Lazaar²,

Mahboub³

2019

Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 20

View full text Add to dashboard Cite

show abstract

“…Medical imaging involves digital images that can be used for analysis by a computer. Therefore, image analysis based on computer-aided diagnosis (CAD) systems for medical image classification is essential in disease detection, screening, and diagnosis [10]. Applying computer-aided screening for colorectal polyp classification and screening with multimedia summarization techniques [11,12] has advantages in increasing the capability of diagnosing colorectal polyps [13].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening

Jinsakul

Tsai

et al. 2019

Mathematics

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One of the leading forms of cancer is colorectal cancer (CRC), which is responsible for increasing mortality in young people. The aim of this paper is to provide an experimental modification of deep learning of Xception with Swish and assess the possibility of developing a preliminary colorectal polyp screening system by training the proposed model with a colorectal topogram dataset in two and three classes. The results indicate that the proposed model can enhance the original convolutional neural network model with evaluation classification performance by achieving accuracy of up to 98.99% for classifying into two classes and 91.48% for three classes. For testing of the model with another external image, the proposed method can also improve the prediction compared to the traditional method, with 99.63% accuracy for true prediction of two classes and 80.95% accuracy for true prediction of three classes.

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“…One of such post-processing techniques is convolutional neural network (CNN), which dates back to the 1980s [10]. CNN has been applied in many fields including handwriting [21] or face recognition [20], and object recognition [23] and classification [24]. Additionally, super-resolution CNN (SRCNN) has more recently been proposed to generate higher resolution images out of low resolution versions [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Super Resolution Convolutional Neural Networks for Increasing Spatial Resolution of $$^{1}$$ H Magnetic Resonance Spectroscopic Imaging

Cengiz

Valdés-Hernández

Öztürk-Işık

2017

Communications in Computer and Information Science

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Proton magnetic resonance spectroscopic imaging (1 H-MRSI) provides noninvasive information regarding metabolic activity within the tissues. One of the main problems of MRSI is low spatial resolution due to clinical scan time limitations. Advanced post-processsing algorithms, like convolutional neural networks (CNN) might help with generation of super resolution MR spectroscopic images. In this study, the application of super resolution convolutional neural networks (SRCNN) for increasing the MRSI spatial resolution is presented. FLAIR, T1 weighted and T2 weighted MR images were used in training the SRCNN scheme. The spatial resolution of MRSI images were increased by using the model trained with the anatomical MR images. The results of the proposed technique were compared with bicubic resampling in terms of peak signal to noise ratio, structure similarity index, root mean square error, relative polar edge coherence, and visual information fidelity pixel. Our results indicated that SRCNN would contribute to reconstructing higher resolution MRSI.

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A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images

Cited by 76 publications

References 17 publications

Comparative study of performing features applied in CNN architectures

Comparative study of performing features applied in CNN architectures

Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening

Super Resolution Convolutional Neural Networks for Increasing Spatial Resolution of $$^{1}$$ H Magnetic Resonance Spectroscopic Imaging

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