GI-Net: Anomalies Classification in Gastrointestinal Tract through Endoscopic Imagery with Deep Learning

Gamage, Chathurika; Wijesinghe, Isuru; Chitraranjan, Charith; Perera, Indika

doi:10.1109/mercon.2019.8818929

Cited by 40 publications

(7 citation statements)

References 9 publications

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“…Using the Kvasir v2 datasets, the modified VGG16 model is compared with other models in classifying GIT diseases based on results reported in the article showed in Table 5 . The Densenet-201 and ResNet-18 models that are reported in the reference [ 34 ] achieved an accuracy of 90.74% and 88.43%. Both the models are trained for more than 400 epochs, and it has taken roughly 10 hours to complete training.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed models VGG16, GoogLeNet, and ResNet-18 reported the training time of 1 hour 50 minutes, 1 hour, 7, and 57 minutes, respectively. The literature found that DenseNet-201 [ 34 ] and ResNet-18 [ 34 ] have been trained for more than 10 hours. The ROC curve in Figure 15(a) depicts the tradeoff between true-positive and false-positive rates.…”

Section: Resultsmentioning

confidence: 99%

“…The model reported in [35] has provided an accuracy of 96.11%, which is very close to the proposed method reported in Table 5. But the said model uses the three stages model of baseline, Inception-V3, and VGG [34] 88.43 Baseline+Inceptionv3 + VGGNet [35] 96.11 Ensemble model [36] 93.7 Logistic regression tree [29] 94.2 Proposed method 96.33 [34] and ResNet-18 [34] have been trained for more than 10 hours. The ROC curve in Figure 15(a) depicts the tradeoff between true-positive and false-positive rates.…”

Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

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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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“…Song et al 26 proposed a computer-aided diagnostic system with 50 layer convolution neural network backbone and achieved comparable performance with human experts on colorectal polyp histology prediction. Gamage et al 27 proposed a ensemble of DenseNet-201 with an artificial neural network to predict anomalies of the digestive tract diseases in eight classes. Their ensemble method achieved 95.28% accuracy.…”

Section: Endoscopic Image Classification and Segmentationmentioning

confidence: 99%

Improving Image Classification of Gastrointestinal Endoscopy Using Curriculum Self-Supervised Learning

Guo

Hosseini

Somayajula

et al. 2023

Preprint

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Endoscopy, a widely used medical procedure for examining the gastrointestinal (GI) tract to detect potential disorders, poses challenges in manual diagnosis due to non-specific symptoms and difficulties in accessing affected areas. While supervised machine learning models have proven effective in assisting clinical diagnosis of GI disorders, the scarcity of image-label pairs created by medical experts limits their availability. To address these limitations, we propose a curriculum self-supervised learning framework inspired by human curriculum learning. Our approach leverages the HyperKvasir dataset, which comprises 100k unlabeled GI images for pre-training and 10k labeled GI images for fine-tuning. By adopting our proposed method, we achieved an impressive top-1 accuracy of 88.92% and an F1 score of 73.39%. This represents a 2.1% increase over vanilla SimSiam for the top-1 accuracy and a 1.9% increase for the F1 score. The combination of self-supervised learning and a curriculum-based approach demonstrates the efficacy of our framework in advancing the diagnosis of GI disorders. Our study highlights the potential of curriculum self-supervised learning in utilizing unlabeled GI tract images to improve the diagnosis of GI disorders, paving the way for more accurate and efficient diagnosis in GI endoscopy.

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“…A z-line indica a junc ¸ão esofagogástrica entre a mucosa escamosa do esôfago e a mucosa do estômago; o pylorus conecta o estômago ao intestino delgado. Já o cecum representa, para a colonoscopia (endoscopia gástrica comec ¸ando pelo reto), a região final do exame, o fim do intestino grosso [Gamage et al 2019]. Essas marcac ¸ões também podem ser regiões típicas de presenc ¸a de patologias como úlceras ou inflamac ¸ões [Pogorelov et al 2017].…”

Section: Introduc ¸ãOunclassified

Classification of anatomical landmarks in the gastrointestinal tract with endoscopy images utilizing convolutional neural networks and triplet loss

Paiva

Lima

Bráz

et al. 2021

ENIAC

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De acordo com a Organização Mundial de Saúde, anualmente são contabilizadas 8 milhões de mortes devido a doenças do trato gastrointestinal. A detecção automática das marcações anatômicas é uma tarefa que pode auxiliar profissionais da área da saúde, reduzindo custo e tempo em exames exploratórios. Sistemas de detecção e diagnóstico auxiliados por computador têm sido vastamente explorado no âmbito científico. No entanto é necessário muito poder de processamento para atingir resultados satisfatórios. Com o intuito de contornar esse problema, este trabalho utiliza uma Rede Neural Convolucional simples em conjunto da função de custo Triplet Loss para extrair características de imagens de 3 marcações anatômicas gastrointestinais (z-line, pylorus e cecum) para classificação dessas imagens. Para o treinamento é utilizada a base de dados Kvasir-v2, obtendo 96,60% de Precisão, 97,71% de Acurácia, 96,91% de Especificidade, 98,61% de Sensibilidade e um F1-score de 97,59%.

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GI-Net: Anomalies Classification in Gastrointestinal Tract through Endoscopic Imagery with Deep Learning

Cited by 40 publications

References 9 publications

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

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

Improving Image Classification of Gastrointestinal Endoscopy Using Curriculum Self-Supervised Learning

Classification of anatomical landmarks in the gastrointestinal tract with endoscopy images utilizing convolutional neural networks and triplet loss

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