Gastrointestinal tract disease segmentation and classification in wireless capsule endoscopy using intelligent deep learning model

Raut, Vrushali; Gunjan, Reena; Shete, V. V.; Eknath, Upasani Dhananjay

doi:10.1080/21681163.2022.2099298

Cited by 14 publications

(4 citation statements)

References 43 publications

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“…( 9), 1 indicates the existing iteration and L shows an overall amount of iterations. 𝑐 2 and c 3 are uniformly generated random numbers in [1,0]. The direction of j th variable following location movement to negative or positive infinity and step size are defined as 𝑐 2 and c 3 .…”

Section: Hyperparameter Tuning Using Mssamentioning

confidence: 99%

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Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images

et al. 2023

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Nowadays, the analysis of gastrointestinal (GI) tract disease utilzing endoscopic image classification becomes an active research activity from the biomedical sector. The latest technology in medical imaging is Wireless Capsule Endoscopy (WCE) for diagnosing gastrointestinal diseases namely bleeding, ulcer, polyp, and so on. Manual diagnoses will be time taking and tough for the medical practitioner; thus, the authors have designed computerized approaches for classifying and detecting such diseases. Many research groups presented various machine learning (ML) and image processing methods for classifying GI tract diseases in recent times. Conventional data augmentation and image processing methods are integrated with adjusted pre-trained deep convolutional neural networks (CNNs) for classifying diseases in the GI tract from WCI images. This study presents a Modified Salp Swarm Algorithm with Deep Learning based Gastrointestinal Tract Disease Classification (MSSADL-GITDC) on Endoscopic Images. The presented MSSADL-GITDC technique mainly focuses on the examination of WCE images for GIT classification. To accomplish this, the presented MSSADL-GITDC technique applies median filtering (MF) technique for image smoothening. The presented MSSADL-GITDC technique designs improved capsule network (CapsNet) model for feature extraction where the CapsNet model is modified by the class attention layer (CAL). Moreover, MSSA based hyperparameter tuning process is performed to improve the efficiency of the improved CapsNet model. For GIT classification, deep belief network with extreme learning machine (DBN-ELM) was used. Finally, backpropagation is applied for supervised fine tuning of the DBN-ELM model. The experimental validation of the MSSADL-GITDC technique takes place on Kvasir-V2 database reported the betterment of the MSSADL-GITDC technique on GIT classification with maximum accuracy of 98.03%.

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Section: Hyperparameter Tuning Using Mssamentioning

confidence: 99%

“…Gastrointestinal (GI) diseases are increasingly common in the human digestive system. Some of the common factors of mortality are colorectal cancer, Stomach cancer, and esophageal cancer [1][2][3]. Generally, Endoscopy is necessary to diagnose diseases and it is the initial step in identifying GI tract diseases [4].…”

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confidence: 99%

Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images

et al. 2023

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“…The prediction accuracy has been resolved by the quality and size of the database, model framework, and hyperparameter of architecture. The main challenge of the CNN approach was the need for a massive quantity of data to make a robust model [15,16]. During the medical sector, the quantity of test and training data accessible for developing a powerful system has been restricted.…”

Section: Introductionmentioning

confidence: 99%

Endoscopic Image Analysis for Gastrointestinal Tract Disease Diagnosis Using Nature Inspired Algorithm With Deep Learning Approach

Alruban,

Alabdulkreem,

Eltahir

et al. 2023

IEEE Access

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Endoscopic image analysis has played a pivotal function in the diagnosis and management of gastrointestinal (GI) tract diseases. Gastrointestinal endoscopy is a medical procedure where a flexible tube with an endoscope (camera) is inserted into the GI tract to visualize the inner lining of the colon, esophagus, stomach, and small intestine. The videos and images attained during endoscopy provide valuable data for detecting and monitoring a large number of GI diseases. Computer-assisted automated diagnosis technique helps to achieve accurate diagnoses and provide the patient the relevant medical care. Machine learning (ML) and deep learning (DL) methods have been exploited to endoscopic images for classifying diseases and providing diagnostic support. Convolutional Neural Networks (CNN) and other DL algorithms can learn to discriminate between various kinds of GI lesions based on visual properties. This study presents an Endoscopic Image Analysis for Gastrointestinal Tract Disease Diagnosis using an inspired Algorithm with Deep Learning (EIAGTD-NIADL) technique. The EIAGTD-NIADL technique intends to examine the endoscopic images using nature nature-inspired algorithm with a DL model for gastrointestinal tract disease detection and classification. To pre-process the input endoscopic images, the EIAGTD-NIADL technique uses a bilateral filtering (BF) approach. For feature extraction, the EIAGTD-NIADL technique applies an improved ShuffleNet model. To improve the efficacy of the improved ShuffleNet model, the EIAGTD-NIADL technique uses an improved spotted hyena optimizer (ISHO) algorithm. Finally, the classification process is performed by the use of the stacked long short-term memory (SLSTM) method. The experimental outcomes of the EIAGTD-NIADL system can be confirmed on benchmark medical image datasets. The obtained outcomes demonstrate the promising results of the EIAGTD-NIADL approach over other models.

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“…Yet, the development of effective computer-aided diagnosis (CAD) technologies that can identify and categorize numerous gastrointestinal disorders in a fully automated manner might reduce these diagnostic obstacles to a great extent [9]. Computer-aided diagnosis technologies can be of great value by aiding medical personnel in making accurate diagnoses and identifying appropriate therapies for serious medical diseases in their early stages [10,11]. Over the past few years, the performance of diagnostic-based artificial intelligence (AI) computer-aided diagnosis tools in various medical fields has been significantly improved with the use of deep learning algorithms, particularly artificial neural networks (ANNs) [12].…”

Section: Introductionmentioning

confidence: 99%

A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

Haseeb,

Khan,

Alhaisoni

et al. 2023

CMC

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Diagnosing gastrointestinal cancer by classical means is a hazardous procedure. Years have witnessed several computerized solutions for stomach disease detection and classification. However, the existing techniques faced challenges, such as irrelevant feature extraction, high similarity among different disease symptoms, and the leastimportant features from a single source. This paper designed a new deep learning-based architecture based on the fusion of two models, Residual blocks and Auto Encoder. First, the Hyper-Kvasir dataset was employed to evaluate the proposed work. The research selected a pre-trained convolutional neural network (CNN) model and improved it with several residual blocks. This process aims to improve the learning capability of deep models and lessen the number of parameters. Besides, this article designed an Auto-Encoder-based network consisting of five convolutional layers in the encoder stage and five in the decoder phase. The research selected the global average pooling and convolutional layers for the feature extraction optimized by a hybrid Marine Predator optimization and Slime Mould optimization algorithm. These features of both models are fused using a novel fusion technique that is later classified using the Artificial Neural Network classifier. The experiment worked on the HyperKvasir dataset, which consists of 23 stomach-infected classes. At last, the proposed method obtained an improved accuracy of 93.90% on this dataset. Comparison is also conducted with some recent techniques and shows that the proposed method's accuracy is improved.

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Gastrointestinal tract disease segmentation and classification in wireless capsule endoscopy using intelligent deep learning model

Cited by 14 publications

References 43 publications

Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images

Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images

Endoscopic Image Analysis for Gastrointestinal Tract Disease Diagnosis Using Nature Inspired Algorithm With Deep Learning Approach

A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

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