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Diagnosis of primary clear cell carcinoma of the liver based on faster region-based convolutional neural network

Chinese Medical Journal 2023年第22期136卷 2706-2711页

作者： Bin Liu Jianfei Li Xue Yang Feng Chen Yanyan Zhang Hongjun Li Department of Radiology Beijing YouAn Hospital Capital Medical UniversityBeijing 100069China Department of Radiology Civil Aviation General HospitalBeijing 100123China Extenics Specialized Committee Chinese Association of Artificial IntelligenceBeijing 100876China

Background:Distinguishing between primary clear cell carcinoma of the liver(PCCCL)and common hepatocellular carcinoma(CHCC)through traditional inspection methods before the operation is difficult.This study aimed to establish a faster region-based convolutional neural network(RCNN)model for the accurate differential diagnosis of PCCCL and CHCC.Methods:In this study,we collected the data of 62 patients with PCCCL and 1079 patients with CHCC in Beijing YouAn Hospital from June 2012 to May 2020.A total of 109 patients with CHCC and 42 patients with PCCCL were randomly divided into the training validation set and the test set in a ratio of 4:1.The faster RCNN was used for deep learning of patients’data in the training validation set,and established a convolutional neural network model to distinguish PCCCL and CHCC.The accuracy,average precision,and the recall of the model for diagnosing PCCCL and CHCC were used to evaluate the detection performance of the faster RCNN algorithm.Results:A total of 4392 images of 121 patients(1032 images of 33 patients with PCCCL and 3360 images of 88 patients with CHCC)were uesd in test set for deep learning and establishing the model,and 1072 images of 30 patients(320 images of nine patients with PCCCL and 752 images of 21 patients with CHCC)were used to test the model.The accuracy of the model for accurately diagnosing PCCCL and CHCC was 0.962(95%confidence interval[CI]:0.931-0.992).The average precision of the model for diagnosing PCCCL was 0.908(95%CI:0.823-0.993)and that for diagnosing CHCC was 0.907(95%CI:0.823-0.993).The recall of the model for diagnosing PCCCL was 0.951(95%CI:0.916-0.985)and that for diagnosing CHCC was 0.960(95%CI:0.854-0.962).The time to make a diagnosis using the model took an average of 4 s for each patient.Conclusion:The faster RCNN model can accurately distinguish PCCCL and CHCC.This model could be important for clinicians to make appropriate treatment plans for patients with PCCCL or CHCC.

关键词： Primary clear cell carcinoma of the liver Common hepatocellular carcinoma Differential diagnosis faster RCNN CT faster region-based convolutional neural network

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Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network

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Chinese Medical Journal 2019年第23期132卷 2795-2803页

作者： Shang-Long Liu Shuo Li Yu-Ting Guo Yun-Peng Zhou Zheng-Dong Zhang Shuai Li Yun Lu Department of General Surgery Affiliated Hospital of Qingdao UniversityQingdaoShandong 266003China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China

Background:Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer.This study was performed to develop an automatic and accurate imaging processing technique system,allowing this system to read computed tomography(CT)images correctly and make diagnosis of pancreatic cancer faster.Methods:The establishment of the artificial intelligence(AI)system for pancreatic cancer diagnosis based on sequential contrastenhanced CT images were composed of two processes:training and verification.During training process,our study used all 4385 CT images from 238 pancreatic cancer patients in the database as the training data set.Additionally,we used VGG16,which was pretrained in ImageNet and contained 13 convolutional layers and three fully connected layers,to initialize the feature extraction network.In the verification experiment,we used sequential clinical CT images from 238 pancreatic cancer patients as our experimental data and input these data into the faster region-based convolution network(faster R-CNN)model that had completed training.Totally,1699 images from 100 pancreatic cancer patients were included for clinical verification.Results:A total of 338 patients with pancreatic cancer were included in the study.The clinical characteristics(sex,age,tumor location,differentiation grade,and tumor-node-metastasis stage)between the two training and verification groups were insignificant.The mean average precision was 0.7664,indicating a good training ejffect of the faster R-CNN.Sequential contrastenhanced CT images of 100 pancreatic cancer patients were used for clinical verification.The area under the receiver operating characteristic curve calculated according to the trapezoidal rule was 0.9632.It took approximately 0.2 s for the faster R-CNN AI to automatically process one CT image,which is much faster than the time required for diagnosis by an imaging specialist.Conclusions:faster R-CNN AI is an effective and objective method with high accuracy for the diagnosis of panc

关键词： Artificial intelligence Pancreatic cancer Diagnosis faster region-based convolutional neural network

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Ozone Depletion Identification in Stratosphere Through faster region-based convolutional neural network

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Computers, Materials & Continua 2021年第8期68卷 2159-2178页

作者： Bakhtawar Aslam Ziyad Awadh Alrowaili Bushra Khaliq Jaweria Manzoor Saira Raqeeb Fahad Ahmad Department of Computer Sciences Kinnaird College for WomenLahore54000Pakistan Physics Department College of ScienceJouf UniversitySakakaAljouf72341Saudi Arabia Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia

The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially.This study has made ozone depletion identification through classification using faster region-based convolutional neural network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions.Furthermore,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation.The permanent changes in climate are of serious concern.The leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV radiation.This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune systems.We have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through F-RCNN.Furthermore,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping purposes.It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results.The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or non-depleted.Our proposed model presented 93%accuracy,and it outperforms the prevailing techniques.

关键词： Deep learning image processing classification climate variation ozone layer depleted region non-depleted region UV radiation faster region-based convolutional neural network

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