Hybrid classification of diffuse liver diseases in ultrasound images using deep convolutional neural networks

Pasyar, Pezhman; Mahmoudi, Tahereh; Kouzehkanan, Seyedeh-Zahra Mousavi; Ahmadian, Alireza; Arabalibeik, Hossein; Soltanian, Nahid; Radmard, Amir Reza

doi:10.1016/j.imu.2020.100496

Cited by 25 publications

(14 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

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

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.

show abstract

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

“…In this study, Attention U-Net and TAU-Net are used for segmentation of pancreas and the tumor using CT images. To benefit the advantages of both networks, a novel but simple approach based on hybrid models (37, 38) is proposed using a 3D-CNN to aggregate the outputs of these networks. First, three predicted masks, namely pancreas, tumor and background are generated using each of the two segmentation networks leading to six predicted masks as the inputs to the 3D-CNN aggregator network.…”

Section: Methodsmentioning

confidence: 99%

Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors

Mahmoudi

Kouzehkanan

Radmard

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging tumor segmentation tasks is localization of Pancreatic Ductal Adenocarcinoma (PDAC). Exclusive application of conventional methods does not appear promising. Deep learning approaches has achieved great success in the computer aided diagnosis, especially in biomedical image segmentation. This paper introduces a framework based on convolutional neural network (CNN) for segmentation of PDAC mass and surrounding vessels in CT images by incorporating powerful classic features, as well. First, a 3D-CNN architecture is used to localize the pancreas region from the whole CT volume using 3D Local Binary Pattern (LBP) map of the original image. Segmentation of PDAC mass is subsequently performed using 2D attention U-Net and Texture Attention U-Net (TAU-Net). TAU-Net is introduced by fusion of dense Scale-Invariant Feature Transform (SIFT) and LBP descriptors into the attention U-Net. An ensemble model is then used to cumulate the advantages of both networks using a 3D-CNN. In addition, to reduce the effects of imbalanced data, a new loss function is proposed as a weighted combination of three classic losses including Generalized Dice Loss (GDL), Weighted Pixel-Wise Cross Entropy loss (WPCE) and boundary loss. Due to insufficient sample size for vessel segmentation, we used the above-mentioned pre-trained networks and fin-tuned them. Experimental results show that the proposed method improves the Dice score for PDAC mass segmentation in portal-venous phase by 7.52% compared to state-of-the-art methods (from 53.08% to 60.6%) in term of DSC. Besides, three dimensional visualization of the tumor and surrounding vessels can facilitate the evaluation of PDAC treatment response.

show abstract

“…To combine both gliomas information then to reduce weight based on three-dimensional convolutional neural network, the classification approach has produced an accuracy of 96.49% of this dataset. Pasyar et al [22] The author aims to develop a new hybrid classifier, then verify the liver level based on the liver image dataset handling of a deep convolutional neural network. Clarify the weighted possibility of every class, including its majority voting process.…”

Section: Literature Surveymentioning

confidence: 99%

A Comparative Study on Augmented Analytics Using Deep Learning Techniques

Anusha

Kiruthika

2021

Smart Innovation, Systems and Technologies

View full text Add to dashboard Cite

Image augmentation is the most recognized type of data augmentation and intrinsic development for transforming image diversities in the training dataset that belongs to a similar class as the novel image. In the area of image augmentation handling, a collection of operations is shifting, flipping, zooming, cropping, rotation, and transformation in color space. A wide range of applications frequently used the aspects of deep learning are industry, science, and government domain, namely adaptive testing, image classification, computer vision, object detection, and face recognition and has achieved substantial development and accomplishment of deep learning. This study concentrates on the most important challenges present in the image estimation level that have a significant effect on dimension reduction, pooling, and edge detection. The deep learning methods involved here are convolution neural network (CNN), generative adversarial network (GAN), and deep convolution neural network (DCNN). Finally, a comparative study has performed a massive literature survey on various deep learning models.

show abstract

Hybrid classification of diffuse liver diseases in ultrasound images using deep convolutional neural networks

Cited by 25 publications

References 25 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

Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors

A Comparative Study on Augmented Analytics Using Deep Learning Techniques

Contact Info

Product

Resources

About