scholarly journals A Coarse-to-Fine Fully Convolutional Neural Network for Fundus Vessel Segmentation

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 607 ◽  
Author(s):  
Jianwei Lu ◽  
Yixuan Xu ◽  
Mingle Chen ◽  
Ye Luo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Original Data ◽  
Vessel Segmentation ◽  
Retinal Diseases ◽  
Fundus Images ◽  
The Neural Network ◽  
Coarse To Fine

Fundus vessel analysis is a significant tool for evaluating the development of retinal diseases such as diabetic retinopathy and hypertension in clinical practice. Hence, automatic fundus vessel segmentation is essential and valuable for medical diagnosis in ophthalmopathy and will allow identification and extraction of relevant symmetric and asymmetric patterns. Further, due to the uniqueness of fundus vessel, it can be applied in the field of biometric identification. In this paper, we remold fundus vessel segmentation as a task of pixel-wise classification task, and propose a novel coarse-to-fine fully convolutional neural network (CF-FCN) to extract vessels from fundus images. Our CF-FCN is aimed at making full use of the original data information and making up for the coarse output of the neural network by harnessing the space relationship between pixels in fundus images. Accompanying with necessary pre-processing and post-processing operations, the efficacy and efficiency of our CF-FCN is corroborated through our experiments on DRIVE, STARE, HRF and CHASE DB1 datasets. It achieves sensitivity of 0.7941, specificity of 0.9870, accuracy of 0.9634 and Area Under Receiver Operating Characteristic Curve (AUC) of 0.9787 on DRIVE datasets, which surpasses the state-of-the-art approaches.

scholarly journals Radiomics-Based Features for Prediction of Histological Subtypes in Central Lung Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Huanhuan Li ◽  
Long Gao ◽  
He Ma ◽  
Dooman Arefan ◽  
Jiachuan He ◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Histological Subtypes ◽  
Promising Tool ◽  
Contrast Enhanced Ct ◽  
The Neural Network ◽  
Contrast Enhanced ◽  
Enhanced Ct

ObjectivesTo evaluate the effectiveness of radiomic features on classifying histological subtypes of central lung cancer in contrast-enhanced CT (CECT) images.Materials and MethodsA total of 200 patients with radiologically defined central lung cancer were recruited. All patients underwent dual-phase chest CECT, and the histological subtypes (adenocarcinoma (ADC), squamous cell carcinoma (SCC), small cell lung cancer (SCLC)) were confirmed by histopathological samples. 107 features were used in five machine learning classifiers to perform the predictive analysis among three subtypes. Models were trained and validated in two conditions: using radiomic features alone, and combining clinical features with radiomic features. The performance of the classification models was evaluated by the area under the receiver operating characteristic curve (AUC).ResultsThe highest AUCs in classifying ADC vs. SCC, ADC vs. SCLC, and SCC vs. SCLC were 0.879, 0.836, 0.783, respectively by using only radiomic features in a feedforward neural network.ConclusionOur study indicates that radiomic features based on the CECT images might be a promising tool for noninvasive prediction of histological subtypes in central lung cancer and the neural network classifier might be well-suited to this task.

scholarly journals Predicting Retinal Diseases using Efficient Image Processing and Convolutional Neural Network (CNN)

2021 ◽  
pp. 221-227
Author(s):  
Asif Mohammad ◽  
Mahruf Zaman Utso ◽  
Shifat Bin Habib ◽  
Amit Kumar Das
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Retinal Images ◽  
Retinal Diseases ◽  
Medical Sector ◽  
The Neural Network ◽  
Computational Technology

Neural networks in image processing are becoming a more crucial and integral part of machine learning as computational technology and hardware systems are advanced. Deep learning is also getting attention from the medical sector as it is a prominent process for classifying diseases.  There is a lot of research to predict retinal diseases using deep learning algorithms like Convolutional Neural Network (CNN). Still, there are not many researches for predicting diseases like CNV which stands for choroidal neovascularization, DME, which stands for Diabetic Macular Edema; and DRUSEN. In our research paper, the CNN (Convolutional Neural Networks) algorithm labeled the dataset of OCT retinal images into four types: CNV, DME, DRUSEN, and Natural Retina. We have also done several preprocessing on the images before passing these to the neural network. We have implemented different models for our algorithm where individual models have different hidden layers.  At the end of our following research, we have found that our algorithm CNN generates 93% accuracy.

scholarly journals Automatic identification of triple negative breast cancer in ultrasonography using a deep convolutional neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Heng Ye ◽  
Jing Hang ◽  
Meimei Zhang ◽  
Xiaowei Chen ◽  
Xinhua Ye ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Operating Characteristic ◽  
Triple Negative ◽  
Characteristic Curve ◽  
Deep Convolutional Neural Network ◽  
Automatic Identification ◽  
Ultrasound Images ◽  
Clinical Tool

AbstractTriple negative (TN) breast cancer is a subtype of breast cancer which is difficult for early detection and the prognosis is poor. In this paper, 910 benign and 934 malignant (110 TN and 824 NTN) B-mode breast ultrasound images were collected. A Resnet50 deep convolutional neural network was fine-tuned. The results showed that the averaged area under the receiver operating characteristic curve (AUC) of discriminating malignant from benign ones were 0.9789 (benign vs. TN), 0.9689 (benign vs. NTN). To discriminate TN from NTN breast cancer, the AUC was 0.9000, the accuracy was 88.89%, the sensitivity was 87.5%, and the specificity was 90.00%. It showed that the computer-aided system based on DCNN is expected to be a promising noninvasive clinical tool for ultrasound diagnosis of TN breast cancer.

scholarly journals Auto-detection of strong gravitational lenses using convolutional neural networks

2018 ◽  
Vol 2 ◽  
pp. 1 ◽  
Author(s):  
James Pearson ◽  
Clara Pennock ◽  
Tom Robinson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Area Under Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Automated Detection ◽  
Gravitational Lenses ◽  
Simulated Image ◽  
Operating Characteristic Curve

We propose a method for the automated detection of strong galaxy-galaxy gravitational lenses in images, utilising a convolutional neural network (CNN) trained on 210 000 simulated galaxy-galaxy lens and non-lens images. The CNN, named LensFinder, was tested on a separate 210 000 simulated image catalogue, with 95% of images classied with at least 98.6% certainty. An accuracy of over 98% was achieved and an area under curve of 0.9975 was determined from the resulting receiver operating characteristic curve. A regional CNN, R-LensFinder, was trained to label lens positions in images, perfectly labelling 80% while partially labelling another 10% correctly.

scholarly journals Development and Evaluation of an AI System for COVID-19 Diagnosis

2020 ◽  
Author(s):  
Cheng Jin ◽  
Weixiang Chen ◽  
Yukun Cao ◽  
Zhanwei Xu ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Chest Ct ◽  
Large Dataset ◽  
Operating Characteristic Curve ◽  
Timely Treatment

AbstractEarly detection of COVID-19 based on chest CT will enable timely treatment of patients and help control the spread of the disease. With rapid spreading of COVID-19 in many countries, however, CT volumes of suspicious patients are increasing at a speed much faster than the availability of human experts. Here, we propose an artificial intelligence (AI) system for fast COVID-19 diagnosis with an accuracy comparable to experienced radiologists. A large dataset was constructed by collecting 970 CT volumes of 496 patients with confirmed COVID-19 and 260 negative cases from three hospitals in Wuhan, China, and 1,125 negative cases from two publicly available chest CT datasets. Trained using only 312 cases, our diagnosis system, which is based on deep convolutional neural network, is able to achieve an accuracy of 94.98%, an area under the receiver operating characteristic curve (AUC) of 97.91%, a sensitivity of 94.06%, and a specificity of 95.47% on an independent external verification dataset of 1,255 cases. In a reader study involving five radiologists, only one radiologist is slightly more accurate than the AI system. The AI system is two orders of magnitude faster than radiologists and the code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19.

scholarly journals Blood Vessel Segmentation from Fundus Images Using Modified U-net Convolutional Neural Network

2020 ◽  
Vol 8 (1) ◽  
pp. 21-25
Author(s):  
Afolabi O. Joshua ◽  
◽  
Fulufhelo V. Nelwamondo ◽  
Gugulethu Mabuza-Hocquet
Keyword(s):  
Neural Network ◽  
Blood Vessel ◽  
Convolutional Neural Network ◽  
Vessel Segmentation ◽  
Fundus Images ◽  
Blood Vessel Segmentation
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