3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography

Tan, James S.; Gao, Yongfeng; Zhang, Liang; Cao, Weiguo; Pomeroy, Marc J.; Huo, Yumei; Li, Lihong; Barish, Matthew A.; Abbasi, Almas; Pickhardt, Perry J.

doi:10.1109/tmi.2019.2963177

Cited by 86 publications

(54 citation statements)

References 48 publications

(70 reference statements)

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“…Sum variance can be defined as an indirect parameter of gray level textural dispersion around the mean. For details on the precise mathematical algorithm for the calculation of textural features, the reader is referred to previous publications (Ou et al, 2014; Mapayi et al, 2015; Tan et al, 2016, 2020; Dincic et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Effects of Iron Oxide Nanoparticles on Structural Organization of Hepatocyte Chromatin: Gray Level Co-occurrence Matrix Analysis

et al. 2021

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Section: Methodsmentioning

confidence: 99%

Effects of Iron Oxide Nanoparticles on Structural Organization of Hepatocyte Chromatin: Gray Level Co-occurrence Matrix Analysis

et al. 2021

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“…The combination of ANNs and CT has also been used in the diagnosis of cancer metastasis and invasion depth[ 53 - 56 ], which are also important indicators of patient prognosis. A study even reported that CNN-based systems could distinguish benign and malignant lesions by CT[ 57 ]. More possible development directions of CT were discussed.…”

Section: Achievements Of Ann Research In Gi Diseasesmentioning

confidence: 99%

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Cao¹,

Zhang²,

Wei³

et al. 2021

WJG

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Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.

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“…This approach takes longer time with 12000 iterations which make it more complex and time consuming as well as using larger dataset. The CNN model has been implemented [5] with Gray Level Co-occurrence Matrix(GLCM) [44] to assists the Computer Aided Diagnoses (CADx) [45] system for the classification of all types of medical images like MRI and CT. The proposed method was used to extract and convert the information of irregular segmentation regions of the image into fixed-size GLCM input for CNN.…”

Section: Related Workmentioning

confidence: 99%

BHCNet: Neural Network-Based Brain Hemorrhage Classification Using Head CT Scan

et al. 2021

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Brain Hemorrhage is the eruption of the brain arteries due to high blood pressure or blood clotting that could be a cause of traumatic injury or death. It is the medical emergency in which a doctor also need years of experience to immediately diagnose the region of the internal bleeding before starting the treatment. In this study, the deep learning models Convolutional Neural Network (CNN), hybrid models CNN+LSTM and CNN+GRU are proposed for the Brain Hemorrhage classification. The 200 head CT scan images dataset is used to boost the accuracy rate and computational power of the deep learning models. The major aim of this study is to use the abstraction power of deep learning on a set of fewer images because in most crucial cases extensive datasets are not available on the spot. The image augmentation and imbalancing the dataset methods are adopted with CNN model to design a unique architecture and named as Brain Hemorrhage Classification based on Neural Network (BHCNet). The performance of the proposed approach are analyzed in terms of accuracy, precision, sensitivity, specificity and F1-score. Further, the experimental results are evaluated by comparative analyses of the balanced and imbalanced dataset with CNN, CNN+LSTM and CNN+GRU models. The promising results are achieved with CNN by imbalancing the dataset and gain highest accuracy that outperforms the hybrid CNN+LSTM and CNN+GRU models. The results reveals the effectiveness of the proposed model for accurate prediction to save the life of the patient in the meantime and fast employment in the real life scenario.

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3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography

Cited by 86 publications

References 48 publications

Effects of Iron Oxide Nanoparticles on Structural Organization of Hepatocyte Chromatin: Gray Level Co-occurrence Matrix Analysis

Effects of Iron Oxide Nanoparticles on Structural Organization of Hepatocyte Chromatin: Gray Level Co-occurrence Matrix Analysis

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

BHCNet: Neural Network-Based Brain Hemorrhage Classification Using Head CT Scan

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