Anatomical classification of upper gastrointestinal organs under various image capture conditions using AlexNet

Igarashi, Shohei; Sasaki, Yoshihiro; Mikami, Taisei; Sakuraba, Hirotake; Fukuda, Shinsaku

doi:10.1016/j.compbiomed.2020.103950

Cited by 65 publications

(44 citation statements)

References 26 publications

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“…The duo-deep model has been employed for the classification of ulcer, polyp, bleeding [66]. StomachNetwork has been utilized for the classification of Polyps, Ulcer and normal images with 0.96 accuracy [67]. The proposed method classified the esophagitis, colon polyps, normal, bleeding, and UC (ulcers) images with the highest prediction scores compared to existing works.…”

Section: Classification Results On Private Collected Imagesmentioning

confidence: 99%

3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks

Amin

Sharif

Gul

et al. 2021

Complex Intell. Syst.

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Wireless capsule endoscopy (WCE) might move through human body and captures the small bowel and captures the video and require the analysis of all frames of video due to which the diagnosis of gastrointestinal infections by the physician is a tedious task. This tiresome assignment has fuelled the researcher’s efforts to present an automated technique for gastrointestinal infections detection. The segmentation of stomach infections is a challenging task because the lesion region having low contrast and irregular shape and size. To handle this challenging task, in this research work a new deep semantic segmentation model is suggested for 3D-segmentation of the different types of stomach infections. In the segmentation model, deep labv3 is employed as a backbone of the ResNet-50 model. The model is trained with ground-masks and accurately performs pixel-wise classification in the testing phase. Similarity among the different types of stomach lesions accurate classification is a difficult task, which is addressed in this reported research by extracting deep features from global input images using a pre-trained ResNet-50 model. Furthermore, the latest advances in the estimation of uncertainty and model interpretability in the classification of different types of stomach infections is presented. The classification results estimate uncertainty related to the vital features in input and show how uncertainty and interpretability might be modeled in ResNet-50 for the classification of the different types of stomach infections. The proposed model achieved up to 90% prediction scores to authenticate the method performance.

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Section: Classification Results On Private Collected Imagesmentioning

confidence: 99%

3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks

Amin

Sharif

Gul

et al. 2021

Complex Intell. Syst.

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show abstract

“…The AlexNet model is proposed to classify the upper gastrointestinal organs from the images captured under different conditions. The model achieves an accuracy of 96.5% in upper gastrointestinal anatomical classification [ 17 ]. The author proposed the technique to reduce the review time of endoscopy screening based on the analysis of factorization.…”

Section: Related Workmentioning

confidence: 99%

Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model

Yogapriya¹,

Sumithra

Anitha³

et al. 2021

Computational and Mathematical Methods in Medicine

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Wireless capsule endoscopy is a noninvasive wireless imaging technology that becomes increasingly popular in recent years. One of the major drawbacks of this technology is that it generates a large number of photos that must be analyzed by medical personnel, which takes time. Various research groups have proposed different image processing and machine learning techniques to classify gastrointestinal tract diseases in recent years. Traditional image processing algorithms and a data augmentation technique are combined with an adjusted pretrained deep convolutional neural network to classify diseases in the gastrointestinal tract from wireless endoscopy images in this research. We take advantage of pretrained models VGG16, ResNet-18, and GoogLeNet, a convolutional neural network (CNN) model with adjusted fully connected and output layers. The proposed models are validated with a dataset consisting of 6702 images of 8 classes. The VGG16 model achieved the highest results with 96.33% accuracy, 96.37% recall, 96.5% precision, and 96.5% F1-measure. Compared to other state-of-the-art models, the VGG16 model has the highest Matthews Correlation Coefficient value of 0.95 and Cohen’s kappa score of 0.96.

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“…The results showed that the CNN has a good effect to classify the anatomical location of EGD images for stomach and duodenum images, with an area under the curve of 0.99[ 15 ]. Igarashi et al [ 16 ] used AlexNet (a deep learning framework) to retrospectively analyze 85246 original images of EGD images in 441 patients with gastric cancer and developed an anatomical organ classifier. The accuracy rates of the training and validation sets were 0.993 and 0.965, respectively.…”

Section: Ai In Small Intestine Anatomymentioning

confidence: 99%

Artificial intelligence in small intestinal diseases: Application and prospects

Yu¹,

Li²,

Yao³

et al. 2021

WJG

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The small intestine is located in the middle of the gastrointestinal tract, so small intestinal diseases are more difficult to diagnose than other gastrointestinal diseases. However, with the extensive application of artificial intelligence in the field of small intestinal diseases, with its efficient learning capacities and computational power, artificial intelligence plays an important role in the auxiliary diagnosis and prognosis prediction based on the capsule endoscopy and other examination methods, which improves the accuracy of diagnosis and prediction and reduces the workload of doctors. In this review, a comprehensive retrieval was performed on articles published up to October 2020 from PubMed and other databases. Thereby the application status of artificial intelligence in small intestinal diseases was systematically introduced, and the challenges and prospects in this field were also analyzed.

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Anatomical classification of upper gastrointestinal organs under various image capture conditions using AlexNet

Cited by 65 publications

References 26 publications

3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks

3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks

Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model

Artificial intelligence in small intestinal diseases: Application and prospects

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