Holistic Brain Tumor Screening and Classification Based on DenseNet and Recurrent Neural Network

Zhou, Yufan; Li, Zheshuo; Zhu, Hong; Chen, Changyou; Gao, Mingchen; Xu, Kai; Xu, Jinhui

doi:10.1007/978-3-030-11723-8_21

Cited by 67 publications

(50 citation statements)

References 9 publications

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“…A source model trained with widely available 'natural' images can be transferred to a target model that will perform similar tasks but in the medical imaging domain. The learnt feature detectors of these deep architectures as a result of their low-level status can be an alternative and viable (22) MGMT state 94.9/-Grinband et al (19) MGMT state 83/84 Akkus et al (23) 1p19q codeletion status 87.7/-Grinband et al (19) 1p19q codeletion status 92/88 Bonte et al (25) Glioma grading 91.1,93.5/82,86.1 Zhou et al (27) Metastatic/glioma/meningioma 92.1/-Momeni et al (28) Oligendroglioma/astrocytoma 85/92 Afshar et al (29) Glioma/pituitary/meningioma 86.6/-Yu et al (31) EGFR mutation status 76.1/82.8 Wang et al (32) EGFR mutation status 73.9/81 Zhu et al 34Luminal A vs others -/58-65 Ha et al (35) Luminal A vs. B vs. HER2 + vs. Basal 70/87.1 Yoon et al (36) Pathological state, ER, PR, HER2 -/69.7, 97.6, 89.9, 84.2 Zhu et al (37) Occult invasive disease status -/70 Ypsilantis et al (8) Neoadjuvant chemotherapy response 73.4/66.3 Bibault et al (38) Neoadjuvant chemoradiation response 80/72 Chen et al (39) Subtype prediction 80, voting: 92.3/-Trivizakis et al (12) Primary/metastasis 83/80 Cha et al (40) Chemotherapy response -/62-77 Cha et al (41) Chemotherapy response -/62-79 Banerjee et al (42) Subtype prediction 85/-Zhou et al (43) Lymph node metastasis 72.7-93/65-92 IDH1, isocitrate dehydrogenase isozyme 1; MGMT, methylguanine methyltransferase; EGFR, epidermal growth factor receptor; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; ACC, accuracy; AUC, area under the curve.…”

Section: Multi-model Decision Fusionmentioning

confidence: 99%

“…Contemporary artificial intelligence decision support systems have limitations related to the 'ground truth' for the studied region of interest (26,27). The differences in pixel-wise labeling (inter-observer variability and bias) for lesion delineation, uncertainty in the examined anatomical areas of malignances (surrounding area, necrosis), disregarding location-based information of the tumor and dependence on morphological features from ROIs may introduce additional variability and misdirection during the convergence process of a fully automated data-driven AI model.…”

Section: Limitations Of Radiogenomic Researchmentioning

confidence: 99%

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Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

et al. 2020

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The new era of artificial intelligence (AI) has introduced revolutionary data-driven analysis paradigms that have led to significant advancements in information processing techniques in the context of clinical decision-support systems. These advances have created unprecedented momentum in computational medical imaging applications and have given rise to new precision medicine research areas. Radiogenomics is a novel research field focusing on establishing associations between radiological features and genomic or molecular expression in order to shed light on the underlying disease mechanisms and enhance diagnostic procedures towards personalized medicine. The aim of the current review was to elucidate recent advances in radiogenomics research, focusing on deep learning with emphasis on radiology and oncology applications. The main deep learning radiogenomics architectures, together with the clinical questions addressed, and the achieved genetic or molecular correlations are presented, while a performance comparison of the proposed methodologies is conducted. Finally, current limitations, potentially understudied topics and future research directions are discussed. Contents 1. Introduction 2. Research methodology 3. Deep architectures used in current radiogenomics studies 4. Clinical applications of deep learning-based radiogenomics 5. Limitations of radiogenomic research 6. Discussion and future directions

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Section: Multi-model Decision Fusionmentioning

confidence: 99%

Section: Limitations Of Radiogenomic Researchmentioning

confidence: 99%

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

et al. 2020

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“…Besides, the study brought an accuracy of 94.2 % in the classification of Glioma, Meningioma, and Pituitary. Zhou et al [25] purposed a method to use the 3D holistic image directly. First of all, 3D holistic image is converted into the 2D slices in the sequence, and then they applied DenseNet for the extraction of the features from each 2D slice.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor

Noreen

Palaniappan

Qayyum³

et al. 2020

IEEE Access

290

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Brain tumor is a deadly disease and its classification is a challenging task for radiologists because of the heterogeneous nature of the tumor cells. Recently, computer-aided diagnosis-based systems have promised, as an assistive technology, to diagnose the brain tumor, through magnetic resonance imaging (MRI). In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical images. To overcome this problem, this study proposes a method of multi-level features extraction and concatenation for early diagnosis of brain tumor. Two pretrained deep learning models i.e. Inception-v3 and DensNet201 make this model valid. With the help of these two models, two different scenarios of brain tumor detection and its classification were evaluated. First, the features from different Inception modules were extracted from pre-trained Inception-v3 model and concatenated these features for brain tumor classification. Then, these features were passed to softmax classifier to classify the brain tumor. Second, pre-trained DensNet201 was used to extract features from various DensNet blocks. Then, these features were concatenated and passed to softmax classifier to classify the brain tumor. Both scenarios were evaluated with the help of three-class brain tumor dataset that is available publicly. The proposed method produced 99.34 %, and 99.51% testing accuracies respectively with Inception-v3 and DensNet201 on testing samples and achieved highest performance in the detection of brain tumor. As results indicated, the proposed method based on features concatenation using pre-trained models outperformed as compared to existing state-of-the-art deep learning and machine learning based methods for brain tumor classification. INDEX TERMS Deep learning, magnetic resonance imaging, brain tumor classification, pre-trained model, dataset.

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“…Zhou et al [ 44 ] put forward a classifier based on DenseNet and LSTM (DenseNet-LSTM). In this model, features are extracted from MR images with an autoencoder architecture i.e., DenseNet.…”

Section: Dcnns Application In the Classification Of Brain Cancer Imentioning

confidence: 99%

The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey

Shirazi

Fornaciari

McDonnell

et al. 2020

JPM

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In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.

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Holistic Brain Tumor Screening and Classification Based on DenseNet and Recurrent Neural Network

Cited by 67 publications

References 9 publications

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

A Deep Learning Model Based on Concatenation Approach for the Diagnosis of Brain Tumor

The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey

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