Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

Renukadevi, Thangavel; Karunakaran, S.

doi:10.1002/ima.22375

Cited by 15 publications

(5 citation statements)

References 33 publications

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“…Forty-seven studies had the classification of liver or liver lesions as a study aim. Thirty-four of them journal articles [ 56 , 71 , 72 , 74 , 78 , 141 – 146 , 148 – 152 , 154 , 156 – 161 , 164 – 172 , 202 , 203 ], and thirteen are proceedings papers [ 27 , 64 , 65 , 68 , 75 , 82 , 119 , 147 , 153 , 155 , 162 , 163 , 204 ]. For classification of liver or liver lesions, traditional machine learning, e.g., support vector machines and random forest models, and deep learning models were commonly used.…”

Section: Resultsmentioning

confidence: 99%

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

Radiya,

Joakimsen,

Mikalsen

et al. 2023

Eur Radiol

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Objectives Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging? Methods A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography. Results One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians’ intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy. Conclusion Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this. Key Points • ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.

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Section: Resultsmentioning

confidence: 99%

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

Radiya,

Joakimsen,

Mikalsen

et al. 2023

Eur Radiol

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“…A DBN 34 is an advanced category of deep neural network (DNN). A DBN is composed of a visible layer, two or more hidden layers, and output layer.…”

Section: Proposed Methodsmentioning

confidence: 99%

Categorization of breast masses based on deep belief network parameters optimized using chaotic krill herd optimization algorithm for frequent diagnosis of breast abnormalities

Chandraraju¹,

Jeyaprakash²

2022

Int J Imaging Syst Tech

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Several deep learning techniques are utilized for classification of the masses in mammogram images. But the existing method doesnot provide sufficient accuracy . for breast masses classification in mammogram images. In this manuscript, an efficient breast cancer image classification framework utilizing the deep belief network (DBN) through chaotic krill herd optimization (CKHO) algorithm for classification of masses in mammogram images is proposed. Initially the input mammogram images are pre‐processed by altered phase preserving dynamic range compression (APPDRC) method for removing the unwanted noise and artifacts. Then these pre‐processed images are assumed with DBN for classifying that mass in the mammogram images into normal, benign, and malignant lesions. Generally, DBN does not reveal any acceptance of optimization techniques for computing optimal parameters that guarantee an accurate classification. Therefore in this work, proposed CKHO algorithm is employed for optimizing weight parameters of the self‐attention convolutional neural network (SACNN). The simulation process is performed under MATLAB platform. Finally, the proposed DBN with chaotic krill herd optimization algorithm (DBN‐CKHO) attains high accuracy 44.5%, 33.42%, 55.23%, 62.35%, and 43.42% when compared with the existing method such as extreme learning machine classifier with moth flame optimization (ELMC‐MFO), Kernel extreme learning machine classifier with Chaotic salp swarm algorithm (KELMC‐CSSA), Extreme learning machine classifier with fruit fly optimization algorithm (ELMC‐FOA), Kernel extreme learning machine classifier with grasshopper optimization algorithm (KELMC‐GOA) and deep neural network with non‐dominated sorting genetic algorithm‐based classification approach.

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“…Renukadevi and Karunakaran [143] proposed hybridized GOA with Deep Belief Network(DBN) for liver disease classification. The performance of DBN-GOA was evaluated based on real-time and open-source computed tomography (CT) image datasets.…”

Section: ) Grasshopper Optimization Algorithm With Support Vector Rmentioning

confidence: 99%

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

et al. 2021

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Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

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Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

Cited by 15 publications

References 33 publications

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

Categorization of breast masses based on deep belief network parameters optimized using chaotic krill herd optimization algorithm for frequent diagnosis of breast abnormalities

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

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