A Deep Learning Approach for Hepatocellular Carcinoma Grading

2020 ◽  
pp. 353-371
Author(s):  
Vitoantonio Bevilacqua ◽  
Antonio Brunetti ◽  
Gianpaolo Francesco Trotta ◽  
Leonarda Carnimeo ◽  
Francescomaria Marino ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Needle Biopsy ◽  
Regions Of Interest ◽  
Learning Approach ◽  
Cad Systems ◽  
Segmentation Algorithms ◽  
Computer Aided ◽  
Identification And Characterization

Introduction and objective: Computer Aided Decision (CAD) systems based on Medical Imaging could support radiologists in grading Hepatocellular carcinoma (HCC) by means of Computed Tomography (CT) images, thus avoiding medical invasive procedures such as biopsies. The identification and characterization of Regions of Interest (ROIs) containing lesions is an important phase allowing an easier classification in two classes of HCCs. Two steps are needed for the detection of lesioned ROIs: a liver isolation in each CT slice and a lesion segmentation. Materials and methods: Materials consist in abdominal CT hepatic lesion from 18 patients subjected to liver transplant, partial hepatectomy, or US-guided needle biopsy. Several approaches are implemented to segment the region of liver and, then, detect the lesion ROI. Results: A Deep Learning approach using Convolutional Neural Network is followed for HCC grading. The obtained good results confirm the robustness of the segmentation algorithms leading to a more accurate classification.

A Deep Learning Approach for Hepatocellular Carcinoma Grading

2017 ◽  
Vol 7 (2) ◽  
pp. 1-18 ◽  
Author(s):  
Vitoantonio Bevilacqua ◽  
Antonio Brunetti ◽  
Gianpaolo Francesco Trotta ◽  
Leonarda Carnimeo ◽  
Francescomaria Marino ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Needle Biopsy ◽  
Learning Approach ◽  
Lesion Segmentation ◽  
Invasive Procedures ◽  
Abdominal Ct ◽  
Segmentation Algorithms ◽  
Identification And Characterization

Introduction and objective: Computer Aided Decision (CAD) systems based on Medical Imaging could support radiologists in grading Hepatocellular carcinoma (HCC) by means of Computed Tomography (CT) images, thus avoiding medical invasive procedures such as biopsies. The identification and characterization of Regions of Interest (ROIs) containing lesions is an important phase allowing an easier classification in two classes of HCCs. Two steps are needed for the detection of lesioned ROIs: a liver isolation in each CT slice and a lesion segmentation. Materials and methods: Materials consist in abdominal CT hepatic lesion from 18 patients subjected to liver transplant, partial hepatectomy, or US-guided needle biopsy. Several approaches are implemented to segment the region of liver and, then, detect the lesion ROI. Results: A Deep Learning approach using Convolutional Neural Network is followed for HCC grading. The obtained good results confirm the robustness of the segmentation algorithms leading to a more accurate classification.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

scholarly journals Deep Learning Approach for Analysis and Characterization of COVID-19

2022 ◽  
Vol 70 (1) ◽  
pp. 451-468
Author(s):  
Indrajeet Kumar ◽  
Sultan S. Alshamrani ◽  
Abhishek Kumar ◽  
Jyoti Rawat ◽  
Kamred Udham Singh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach

scholarly journals Identification and Characterization of Tropomyosin 3 Associated with Granulin-Epithelin Precursor in Human Hepatocellular Carcinoma

PLoS ONE ◽  
2012 ◽  
Vol 7 (7) ◽  
pp. e40324 ◽  
Author(s):  
Ching Yan Lam ◽  
Chi Wai Yip ◽  
Terence C. W. Poon ◽  
Christine K. C. Cheng ◽  
Eddy W. Y. Ng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Human Hepatocellular Carcinoma ◽  
Identification And Characterization

scholarly journals A computer-aided diagnosis system for lung nodule detection in pulmonary CT images based on LS-SVM and wavelet transformation

2021 ◽  
Author(s):  
Omid Talakoub
Keyword(s):  
Wavelet Transformation ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Regions Of Interest ◽  
Support Vector ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Spherical Structures ◽  
Aided Diagnosis

One of the most important areas of biomedical engineering is medical imaging. Fully automated schemes are currently being explored as Computer-Aided Diagnosis (CAD) systems to provide a second opinion to medical professionals; of these systems, abnormal region detector in medical images is one of the most critical CAD systems in development. The primary motivation in using these systems is due to the fact that reading an enormous number of images is a time-consuming task for the radiologist. This task can be sped up by using a CAD system which highlights abnormal regions of interest. Low false positive rates and high sensitivity are essential requirement[s] of such a system. The initial requirement of processing any organ is an accurate segmentation of the target of interest in the images. A segmentation method based on the wavelet transformation is proposed which accurately extracts lung regions in the thoracic CT images. After this step, an Aritifical Intelligence system, known as Least Squares Support Vector Machine (LS-SVM), is employed to classify nodules within the regions of interest. It is a well known fact that the lung nodules, except the pleural nodules, are mostly spherical structures whereas other structures including blood vessels are shaped as other structures such as tubular. Therfore, an enhancment filter is developed in which spherical structures are accentuated. Processing three different real databases revealed that the proposed system has reached the objective of a CAD system to provide reliable opinion for the doctors in the diagnosis fashion.

scholarly journals Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans

2020 ◽  
Author(s):  
Mohamed Esmail Karar ◽  
Ezz El-Din Hemdan ◽  
Marwa A. Shouman
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Detection Accuracy ◽  
Cad Systems ◽  
X Ray ◽  
Learning Framework ◽  
Learning Classifiers ◽  
Computer Aided ◽  
Aided Diagnosis

Abstract Computer-aided diagnosis (CAD) systems are considered a powerful tool for physicians to support identification of the novel Coronavirus Disease 2019 (COVID-19) using medical imaging modalities. Therefore, this article proposes a new framework of cascaded deep learning classifiers to enhance the performance of these CAD systems for highly suspected COVID-19 and pneumonia diseases in X-ray images. Our proposed deep learning framework constitutes two major advancements as follows. First, complicated multi-label classification of X-ray images have been simplified using a series of binary classifiers for each tested case of the health status. That mimics the clinical situation to diagnose potential diseases for a patient. Second, the cascaded architecture of COVID-19 and pneumonia classifiers is flexible to use different fine-tuned deep learning models simultaneously, achieving the best performance of confirming infected cases. This study includes eleven pre-trained convolutional neural network models, such as Visual Geometry Group Network (VGG) and Residual Neural Network (ResNet). They have been successfully tested and evaluated on public X-ray image dataset for normal and three diseased cases. The results of proposed cascaded classifiers showed that VGG16, ResNet50V2, and Dense Neural Network (DenseNet169) models achieved the best detection accuracy of COVID-19, viral (Non-COVID-19) pneumonia, and bacterial pneumonia images, respectively. Furthermore, the performance of our cascaded deep learning classifiers is superior to other multi-label classification methods of COVID-19 and pneumonia diseases in previous studies. Therefore, the proposed deep learning framework presents a good option to be applied in the clinical routine to assist the diagnostic procedures of COVID-19 infection.

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