Computer-Aided Estimation for the Risk of Development of Gastric Cancer by Image Processing

Sasaki, Yoshihiro; Hada, Ryukichi; Yoshimura, Teizo; Hanabata, Norihiro; Mikami, Taisei; Fukuda, Shinsaku

doi:10.1007/978-3-642-15286-3_19

Cited by 9 publications

(8 citation statements)

References 11 publications

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“…Numerous studies have investigated how to identify gastrointestinal polyps. Research on polyp diagnosis has primarily concentrated on detection, segmentation, and classi cation [3][4][5][6][7][8][9][10][15] [17]. There isn't much research that use computer vision, machine learning, or deep learning to classify polyps.…”

Section: Literature Reviewmentioning

confidence: 99%

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Computer-Aided Polyps Classification from Colonoscopy Using Deep Learning Models

Gangrade,

Sharma,

Sharma

et al. 2023

Preprint

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Medical imaging has advanced to the extent that conditions including stomach ulcers, bleeding, and polyps can be diagnosed using video endoscopy. It takes a lot of time for doctors to follow up on all the images produced by medical video endoscopy. This complicates the use of labor. Automated diagnosis through computer aided approaches to analyze all the resulting images rapidly and accurately. The proposed methodology is innovative in that it seeks to create a system for diagnosing gastrointestinal disorders. The images that are sent into the deep learning networks have all been improved and have had the noise removed. The 5000 images in the Kvasir dataset are evenly split between five different categories affecting the digestive tract: dye-lifted polyps, dyed resection margins, normal cecum, polyps, and ulcerative coliti. Five finely tuned deep convolutional neural network architectures (Xception, ResNet-101, VGG-19, EfficientNetB2v3, and MobineNetV2) with weights from the ImageNet dataset. EffecientNetV2B3 outperformed and achieved accuracy of 96.0%.

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Section: Literature Reviewmentioning

confidence: 99%

“…The images were taken with a narrow-band imaging (NBI) camera. To train their model they suggested a semisupervised approach that makes use of projected depth information taken from Mahmood et al [7]. As a result, the system can segment data and classify it with 87.24 percent accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer-Aided Polyps Classification from Colonoscopy Using Deep Learning Models

Gangrade,

Sharma,

Sharma

et al. 2023

Preprint

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“…The electronic endoscope has allowed us to quantify any element making up a digitized endoscopic image through mathematical processes. Several studies have evaluated the effectiveness of feature extraction for computer-aided diagnosis (CAD) to classify the endoscopic severity of ulcerative colitis 1,2 and to assess the risk of developing gastric cancer among Helicobacter pylori-positive patients 3 . However, the diagnostic accuracy of feature engineering was limited due to the challenges in extracting features for image analysis in gastrointestinal diseases.…”

Section: Introductionmentioning

confidence: 99%

A trial deep learning-based model for four-class histologic classification of colonic tumor from narrow band imaging

Shimizu

Sasaki

Ito

et al. 2023

Preprint

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Narrow band imaging (NBI) has been extensively utilized as a diagnostic tool for colorectal neoplastic lesions. This study aimed to develop a trial deep learning (DL) based four-class classification model for low-grade dysplasia (LGD); high-grade dysplasia or mucosal carcinoma (HGD); superficially invasive submucosal carcinoma (SMs) and deeply invasive submucosal carcinomas (SMd) and evaluate its potential as a diagnostic tool. We collected a total of 1390 NBI images as the dataset, including 53 LGD, 120 HGD, 20 SMs and 17 SMd. A total of 59,8801 patches were trimmed from the lesion and background. A patch-based classification model was built by employing a residual convolutional neural network (CNN) and validated by three-fold cross-validation. The patch-based validation accuracy was 0.876, 0.957, 0.907 and 0.929 in LGD, HGD, SMs and SMd, respectively. The image-level classification algorithm was derived from the patch-based mapping across the entire image domain, attaining accuracies of 0.983, 0.990, 0.964, and 0.992 in LGD, HGD, SMs, and SMd, respectively. Our CNN-based model demonstrated high performance for categorizing the histological grade of dysplasia as well as the depth of invasion in routine colonoscopy, suggesting a potential diagnostic tool with minimal human inputs.

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“…Clinically, artificial intelligence techniques can distinguish between neoplastic and nonneoplastic tissues. Techniques are also available for extracting texture features to evaluate the risk of gastric cancers [10,11]. Colonoscopy images have been used to classify colitis by extracting texture features [12,13].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases

et al. 2021

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Currently, nearly two million patients die of gastrointestinal diseases worldwide. Video endoscopy is one of the latest technologies in the medical imaging field for the diagnosis of gastrointestinal diseases, such as stomach ulcers, bleeding, and polyps. Medical video endoscopy generates many images, so doctors need considerable time to follow up all the images. This creates a challenge for manual diagnosis and has encouraged investigations into computer-aided techniques to diagnose all the generated images in a short period and with high accuracy. The novelty of the proposed methodology lies in developing a system for diagnosis of gastrointestinal diseases. This paper introduces three networks, GoogleNet, ResNet-50, and AlexNet, which are based on deep learning and evaluates them for their potential in diagnosing a dataset of lower gastrointestinal diseases. All images are enhanced, and the noise is removed before they are inputted into the deep learning networks. The Kvasir dataset contains 5,000 images divided equally into five types of lower gastrointestinal diseases (dyed-lifted polyps, normal cecum, normal pylorus, polyps, and ulcerative colitis). In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. The softmax activation function receives the deep feature vector and classifies the input images into five classes. All CNN models achieved superior results. AlexNet achieved an accuracy of 97%, sensitivity of 96.8%, specificity of 99.20%, and AUC of 99.98%.

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Computer-Aided Estimation for the Risk of Development of Gastric Cancer by Image Processing

Cited by 9 publications

References 11 publications

Computer-Aided Polyps Classification from Colonoscopy Using Deep Learning Models

Computer-Aided Polyps Classification from Colonoscopy Using Deep Learning Models

A trial deep learning-based model for four-class histologic classification of colonic tumor from narrow band imaging

Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases

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