Identification of Colon Cancer Using Multi-Scale Feature Fusion Convolutional Neural Network Based on Shearlet Transform

Liang, Meiyan; Ren, Zhuyun; Yang, Jiamiao; Feng, Wenxiang; Li, Bo

doi:10.1109/access.2020.3038764

Cited by 35 publications

(17 citation statements)

References 18 publications

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“…• Pretrained networks and deep networks: It is evident in this article that out of 101 research papers, vast majority of contributions by deep networks were Gaussian blur smoothing [74,76] k-means [67,72], Graph-based [70], Dense UNet [70], Region growing [71,72], Thresholding [74,76,81], CNN [79] Zoom-out features [67], ConvNet features [65,67,76,99], Object graph features [72], Topological features [65], Various texture features [73,79,80], Morphology features [75,79,80] Transfer learning models [73,80,83], Machine learning algorithms [65,67,72,76,80,99], Custom CNN [69,74,78,80], Quasi-supervised learning [73], Neural network [66,74,81], Ensemble CNN [77] Prostate…”

Section: Cervicalmentioning

confidence: 99%

“…Instead of single-center dataset, the model provided better accuracy of 99.13% when datasets were combined. Liang et al [78] presented a method to identify histopathological images of ADC using multi-scale feature fusion CNN-based shearlet transform technique. They achieved detection accuracy of 96% for 8,000 image patches in the training dataset.…”

Section: Colon Adcmentioning

confidence: 99%

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AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

Prabhua¹,

Prasada²,

Antonio³

et al. 2022

Preprint

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Histopathological image analysis is the gold standard to diagnose cancer. Carcinoma is a subtype of cancer that constitutes more than 80% of all cancer cases. Squamous cell carcinoma and adenocarcinoma are two major subtypes of carcinoma, diagnosed by microscopic study of biopsy slides. However, manual microscopic evaluation is a subjective and time-consuming process. Many researchers have reported methods to automate carcinoma detection and classification. The increasing use of artificial intelligence (AI) in the automation of carcinoma diagnosis also reveals a significant rise in the use of deep network models. In this systematic literature review, we present a comprehensive review of the state-of-the-art approaches reported in carcinoma diagnosis using histopathological images. Studies are selected from well-known databases with strict inclusion/exclusion criteria. We have categorized the articles and recapitulated their methods based on specific organs of carcinoma origin. Further, we have summarized pertinent literature on AI methods, highlighted critical challenges and limitations, and provided insights on future research direction in automated carcinoma diagnosis. Out of 101 articles selected, most of the studies experimented on private datasets with varied image sizes, obtaining accuracy between 63% and 100%. Overall, this review highlights the need for a generalized AI-based carcinoma diagnostic system. Additionally, it is desirable to have accountable approaches to extract microscopic features from images of multiple magnifications that should mimic pathologists ′ evaluations.

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Section: Cervicalmentioning

confidence: 99%

Section: Colon Adcmentioning

confidence: 99%

AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

Prabhua¹,

Prasada²,

Antonio³

et al. 2022

Preprint

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“…It demonstrated that hybrid deep learning models may be used to evaluate a reliable cancer diagnosis approach. On the other hand, (Liang et al, 2020) developed a shearlet transform-based multi-scale feature fusion (MFF-CNN) model to recognize histopathology images of colon cancer. It achieved a 96 percent detection performance for colorectal after feature learning and feature fusion.…”

Section: Lung and Colon Cancer Using Hybrid Approachmentioning

confidence: 99%

“…A number of papers have been conducted in the literature, each of which is predicated on a distinct strategy for detecting cancer (Hatuwal and Thapa, 2020) (Tasnim et al, 2021) (Liang et al, 2020) (Chen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

Talukder¹,

Islam²,

Uddin³

et al. 2022

Preprint

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Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action.Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study's findings show that our proposed strategy outperforms existing models significantly. Thus, these models could be applicable in clinics to support the doctor in the diagnosis of cancers.

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“…Recently, the success of CNNs for medical image processing has been reported [18][19][20]. Deep learning methods based on CNNs have been suggested for image preprocessing and feature extraction in BAA tasks, such as fine-tuned CNNs [10], methods based on visual geometry groups (VGGs) [11], UNets for segmentation [12,13], deep residual network (ResNet)-based models [14], and CNNs with attention mechanisms [15].…”

Section: Related Workmentioning

confidence: 99%

Fully Automatic Model Based on SE-ResNet for Bone Age Assessment

Jiang

2021

IEEE Access

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Bone age assessment (BAA) based on hand X-ray imaging is a common clinical practice for investigating disorders and predicting the adult height of a child. However, the traditional manual method is time consuming and prone to obverse variability. There is an urgent need for a fully automatic framework based on deep learning with high performance and efficiency. We propose an end-to-end BAA model based on lossless image compression and a squeeze-and-excitation deep residual network (SE-ResNet). First, we apply the compression module to compress the raw image without losing important features. Second, the SE-ResNet-based model extracts features of the compressed images. Furthermore, the regression model with improved loss function predicts bone age. The experiments on a public dataset reveal that our method outperforms the baseline models. In conclusion, the presented method is a fully automatic and effective solution to process hand X-ray images for BAAs.INDEX TERMS bone age assessment, deep learning, convolutional neural network, residual network, regression.

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Identification of Colon Cancer Using Multi-Scale Feature Fusion Convolutional Neural Network Based on Shearlet Transform

Cited by 35 publications

References 18 publications

AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

AI-based Carcinoma Detection and Classification Using Histopathological Images: A Systematic Review

Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

Fully Automatic Model Based on SE-ResNet for Bone Age Assessment

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