Residual LSTM layered CNN for classification of gastrointestinal tract diseases

Öztürk, Şaban; Özkaya, Umut

doi:10.1016/j.jbi.2020.103638

Cited by 66 publications

(39 citation statements)

References 38 publications

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“…CNN has been employed to perform GI image classification in many research works [ 11 , 12 , 13 ]. These works utilise different types of architectures such as AlexNet [ 11 ], LSTM [ 14 ], and U-Net [ 13 ]. Igarashi et al employed the AlexNet architecture to classify fourteen categories of upper gastrointestinal regions associated with gastric cancer [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

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A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images

et al. 2022

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The first step in the diagnosis of gastric abnormalities is the detection of various abnormalities in the human gastrointestinal tract. Manual examination of endoscopy images relies on a medical practitioner’s expertise to identify inflammatory regions on the inner surface of the gastrointestinal tract. The length of the alimentary canal and the large volume of images obtained from endoscopic procedures make traditional detection methods time consuming and laborious. Recently, deep learning architectures have achieved better results in the classification of endoscopy images. However, visual similarities between different portions of the gastrointestinal tract pose a challenge for effective disease detection. This work proposes a novel system for the classification of endoscopy images by focusing on feature mining through convolutional neural networks (CNN). The model presented is built by combining a state-of-the-art architecture (i.e., EfficientNet B0) with a custom-built CNN architecture named Effimix. The proposed Effimix model employs a combination of squeeze and excitation layers and self-normalising activation layers for precise classification of gastrointestinal diseases. Experimental observations on the HyperKvasir dataset confirm the effectiveness of the proposed architecture for the classification of endoscopy images. The proposed model yields an accuracy of 97.99%, with an F1 score, precision, and recall of 97%, 97%, and 98%, respectively, which is significantly higher compared to the existing works.

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Section: Related Workmentioning

confidence: 99%

“…Ozturk et al presented a residual long short-term memory (LSTM) model for the classification of GI diseases [ 14 ]. It was reported that the residual LSTM structure outperformed the state-of-the-art methods in terms of classification performance.…”

Section: Related Workmentioning

confidence: 99%

A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images

et al. 2022

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“…For the last two decades, machine and deep learning techniques have made a large contribution in handling the information extraction problem from various application areas including medical image analysis and retrieval [19][20][21][22][23], biometrics recognition [24][25][26], disease diagnosis [27,28], agriculture, etc. The following literature study shows the related work on the agricultural sector using machine learning techniques.…”

Section: Literature Surveymentioning

confidence: 99%

Semantic Information Extraction from Multi-Corpora Using Deep Learning

Kumar¹,

Sastry²,

Marriboyina³

et al. 2022

Computers, Materials &Amp; Continua

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Information extraction plays a vital role in natural language processing, to extract named entities and events from unstructured data. Due to the exponential data growth in the agricultural sector, extracting significant information has become a challenging task. Though existing deep learningbased techniques have been applied in smart agriculture for crop cultivation, crop disease detection, weed removal, and yield production, still it is difficult to find the semantics between extracted information due to unswerving effects of weather, soil, pest, and fertilizer data. This paper consists of two parts. An initial phase, which proposes a data preprocessing technique for removal of ambiguity in input corpora, and the second phase proposes a novel deep learning-based long short-term memory with rectification in Adam optimizer and multilayer perceptron to find agricultural-based named entity recognition, events, and relations between them. The proposed algorithm has been trained and tested on four input corpora i.e., agriculture, weather, soil, and pest & fertilizers. The experimental results have been compared with existing techniques and it was observed that the proposed algorithm outperforms Weighted-SOM, LSTM+RAO, PLR-DBN, KNN, and Naïve Bayes on standard parameters like accuracy, sensitivity, and specificity.

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“…The studies described above are summarized in Table 1. DL models have generally been used to develop automatic classification methods [30][31][32]. The deep models have unique benefits and have achieved good results for computer vision problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

Barua

Chan

Doğan

et al. 2021

Entropy

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Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.

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Residual LSTM layered CNN for classification of gastrointestinal tract diseases

Cited by 66 publications

References 38 publications

A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images

A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images

Semantic Information Extraction from Multi-Corpora Using Deep Learning

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

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