Computer assisted gastric abnormalities detection using hybrid texture descriptors for chromoendoscopy images

Ali, Hussam; Yasmin, Mussarat; Sharif, Muhammad; Rehmani, Mubashir Husain

doi:10.1016/j.cmpb.2018.01.013

Cited by 38 publications

(19 citation statements)

References 20 publications

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“…Twenty-seven studies were dedicated to improving diagnostic accuracy in case of colorectal polyps or cancer. Nineteen studies focused on the diagnosis of premalignant or malignant lesions of the upper gastrointestinal tract, [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57] only 4 studies were limited to the small bowel, [58][59][60][61] and 3 studies assessed the entire digestive tract. [62][63][64] Twenty-four studies used specific validation techniques-mainly k-fold cross- validation.…”

Section: Analysis Of Malignant and Premalignant Lesionsmentioning

confidence: 99%

Application of Artificial Intelligence to Gastroenterology and Hepatology

et al. 2020

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Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.

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Section: Analysis Of Malignant and Premalignant Lesionsmentioning

confidence: 99%

Application of Artificial Intelligence to Gastroenterology and Hepatology

et al. 2020

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“…Moreover, computeraided ulcer and erosion detection methods are developed using convolutional neural network (CNN) based architecture [15], completed local binary pattern (LBP), and laplacian pyramid [14], and indexed image based approach [16]. On the other side, tumor recognition methods are developed using textural descriptors in inverse curvelet domain [17], discrete wavelet transform [18], uniform LBP [19], and Gabor filterbank [20]. In [21], a stacked sparse autoencoder with image manifold constraint is proposed to detect polyps from WCE images.…”

Section: Introductionmentioning

confidence: 99%

Multiple Linear Discriminant Models for Extracting Salient Characteristic Patterns in Capsule Endoscopy Images for Multi-Disease Detection

Kundu

Fattah

Wahid

2020

IEEE J. Transl. Eng. Health Med.

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Background: Computer-aided disease detection schemes from wireless capsule endoscopy (WCE) videos have received great attention by the researchers for reducing physicians' burden due to the time-consuming and risky manual review process. While single disease classification schemes are greatly dealt by the researchers in the past, developing a unified scheme which is capable of detecting multiple gastrointestinal (GI) diseases is very challenging due to the highly irregular behavior of diseased images in terms of color patterns. Method: In this paper, a computer-aided method is developed to detect multiple GI diseases from WCE videos utilizing linear discriminant analysis (LDA) based region of interest (ROI) separation scheme followed by a probabilistic model fitting approach. Commonly in training phase, as pixel-labeled images are available in small number, only the image-level annotations are used for detecting diseases in WCE images, whereas pixel-level knowledge, although a major source for learning the disease characteristics, is left unused. In view of learning the characteristic disease patterns from pixel-labeled images, a set of LDA models are trained which are later used to extract the salient ROI from WCE images both in training and testing stages. The intensity patterns of ROI are then modeled by a suitable probability distribution and the fitted parameters of the distribution are utilized as features in a supervised cascaded classification scheme. Results: For the purpose of validation of the proposed multi-disease detection scheme, a set of pixel-labeled images of bleeding, ulcer and tumor are used to extract the LDA models and then, a large WCE dataset is used for training and testing. A high level of accuracy is achieved even with a small number of pixel-labeled images. Conclusion: Therefore, the proposed scheme is expected to help physicians in reviewing a large number of WCE images to diagnose different GI diseases.

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“…To date, AI has been applied in many medical imaging fields, such as endoscopy, pathology as well as CT imaging. AI-assisted endoscopic diagnosis included the extraction of image features[ 7 , 8 ], the detection of early gastric cancer[ 9 - 14 ], the detection of precancerous conditions[ 15 ], the optimization of magnifying endoscopy with narrow-band imaging (M-NBI)[ 16 - 19 ] and the application of Raman endoscopy[ 20 , 21 ]. AI-assisted pathologic diagnosis involved the automatic identification of gastric cancer[ 22 ], the detection of gastric cancer based on the whole slide imaging (WSI)[ 23 - 26 ], the automatic detection of tumor-infiltrating lymphocytes (TILs)[ 27 ] and the segmentation of lesion regions[ 28 - 31 ], while AI-assisted CT diagnosis focused on the identification of preoperative peritoneal metastasis[ 32 ], the detection of perigastric metastatic lymph nodes[ 33 ] and two other new imaging techniques[ 34 , 35 ].…”

Section: Ai In the Diagnosis Of Gastric Cancermentioning

confidence: 99%

Artificial intelligence in gastric cancer: Application and future perspectives

Niu¹,

Zhao²,

Wu³

et al. 2020

WJG

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Gastric cancer is the fourth leading cause of cancer-related mortality across the globe, with a 5-year survival rate of less than 40%. In recent years, several applications of artificial intelligence (AI) have emerged in the gastric cancer field based on its efficient computational power and learning capacities, such as image-based diagnosis and prognosis prediction. AI-assisted diagnosis includes pathology, endoscopy, and computerized tomography, while researchers in the prognosis circle focus on recurrence, metastasis, and survival prediction. In this review, a comprehensive literature search was performed on articles published up to April 2020 from the databases of PubMed, Embase, Web of Science, and the Cochrane Library. Thereby the current status of AI-applications was systematically summarized in gastric cancer. Moreover, future directions that target this field were also analyzed to overcome the risk of overfitting AI models and enhance their accuracy as well as the applicability in clinical practice.

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Computer assisted gastric abnormalities detection using hybrid texture descriptors for chromoendoscopy images

Cited by 38 publications

References 20 publications

Application of Artificial Intelligence to Gastroenterology and Hepatology

Application of Artificial Intelligence to Gastroenterology and Hepatology

Multiple Linear Discriminant Models for Extracting Salient Characteristic Patterns in Capsule Endoscopy Images for Multi-Disease Detection

Artificial intelligence in gastric cancer: Application and future perspectives

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