Intravascular ultrasound image plaque recognition based on improved Resnet network

Gao, Xue; Wang, Yousheng; Sun, Jinge

doi:10.1109/icsp54964.2022.9778687

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…The core of these methods is to find the most representative perturbations through detailed search or optimization. In addition, the influence of occlusion on the output of each method is analyzed by inputting perturbed networks with regular or random occlusion [ 19 , 33 ] and some samples [ 19 , 30 , 31 , 44 ]. For example, reference [ 30 ] used meta-learning as an explanatory factor to establish perturbations to optimize the spatial perturbation mask and, through perturbation experiments, found features that had a greater impact on the output results and gradually established a linearly separable model [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

“…In addition, the influence of occlusion on the output of each method is analyzed by inputting perturbed networks with regular or random occlusion [ 19 , 33 ] and some samples [ 19 , 30 , 31 , 44 ]. For example, reference [ 30 ] used meta-learning as an explanatory factor to establish perturbations to optimize the spatial perturbation mask and, through perturbation experiments, found features that had a greater impact on the output results and gradually established a linearly separable model [ 31 ]. Since it is impossible to see all the perturbations, it is necessary to find representative perturbations.…”

Section: Methodsmentioning

confidence: 99%

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Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process

Gao

Lou

Wang

et al. 2022

Computational Intelligence and Neuroscience

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The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology. AI is commonly defined as the capacity of machines to mimic human cognitive functions. It can tackle jobs with equivalent or superior performance to humans by combining computer science, algorithms, machine learning, and data science. The healthcare system is a dynamic and evolving environment, and medical experts are constantly confronted with new issues, shifting duties, and frequent interruptions. Because of this variation, illness diagnosis frequently becomes a secondary concern for healthcare professionals. Furthermore, clinical interpretation of medical information is a cognitively demanding endeavor. This applies not just to seasoned experts, but also to individuals with varying or limited skills, such as young assistant doctors. In this paper, we proposed the comparative analysis of various state-of-the-art methods of deep learning for medical imaging diagnosis and evaluated various important characteristics. The methodology is to evaluate various important factors such as interpretability, visualization, semantic data, and quantification of logical relationships in medical data. Furthermore, the glaucoma diagnosis system is discussed in detail via qualitative and quantitative approaches. Finally, the applications and future prospects were also discussed.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process

Gao

Lou

Wang

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Thus, automated estimate of the luminal borders and media-adventitia is indispensable to evaluate the degree of stenosis and luminal region during IVUS-assisted diagnoses [6]. Whereas many automatic solutions have been introduced for assisting IVUS segmentation, not any technique has demonstrated its potential to precisely identify the external elastic membrane (EEM) and lumen border and process IVUS images in real-time [7]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Optimal Deep Learning Based Atherosclerotic Plaque Classification on Intravascular Ultrasound Images

2023

IJIES

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Intravascular ultrasound (IVUS) is suitable for evaluating plaque and lesion morphology features and helps to make clinical decisions during the treatment of coronary artery disease (CAD). IVUS remain a gold standard in accessing atherosclerosis plaques, coronary lesions, and stenosis. Even though plaque classification by IVUS is essential for risk stratification, frame-by-frame analysis of an entire vascular segment is labor intensive. In recent times, in the field of deep learning (DL), convolutional neural network (CNN) has been intended to adaptively and automatically determine the spatial hierarchy through backpropagation. The study develops an optimal deep transfer learning based on atherosclerotic plaque and calcification on intravascular ultrasound images (ODTL-APCIUI) technique. The presented ODTL-APCIUI technique aims to classify atherosclerotic plaque and calcification. To accomplish this, the presented ODTL-APCIUI technique preprocesses the IVUS images by Gaussian filtering (GF) technique. In addition, U2Net model is applied for the segmentation process with Adam optimizer based hyperparameter tuning. Moreover, the ODTL-APCIUI technique uses DenseNet-169 model for feature extraction purposes. Furthermore, the ODTL-APCIUI technique exploits stacked autoencoder (SAE) for classification process. Finally, Harris Hawks optimization (HHO) algorithm is exploited for the hyperparameter adjustment of the SAE approach. The performance assessment of ODTL-APCIUI algorithm is tested using medical images and the results are investigated under different metrics. The experimental outcomes demonstrated that the ODTL-APCIUI technique has gained better performance with maximum accuracy of 97.19%, precision of 94.09%, sensitivity of 97.17%, specificity of 97.19%, and F-score of 92.52%.

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Intravascular ultrasound image plaque recognition based on improved Resnet network

Cited by 2 publications

References 2 publications

Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process

Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process

Optimal Deep Learning Based Atherosclerotic Plaque Classification on Intravascular Ultrasound Images

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