scholarly journals Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Akitoshi Shimazaki ◽  
Daiju Ueda ◽  
Antoine Choppin ◽  
Akira Yamamoto ◽  
Takashi Honjo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Chest Radiographs ◽  
Training Dataset ◽  
Segmentation Method ◽  
Test Dataset ◽  
Lung Cancers ◽  
Independent Test Dataset ◽  
Malignant Lesions ◽  
Lung Cancer Detection

AbstractWe developed and validated a deep learning (DL)-based model using the segmentation method and assessed its ability to detect lung cancer on chest radiographs. Chest radiographs for use as a training dataset and a test dataset were collected separately from January 2006 to June 2018 at our hospital. The training dataset was used to train and validate the DL-based model with five-fold cross-validation. The model sensitivity and mean false positive indications per image (mFPI) were assessed with the independent test dataset. The training dataset included 629 radiographs with 652 nodules/masses and the test dataset included 151 radiographs with 159 nodules/masses. The DL-based model had a sensitivity of 0.73 with 0.13 mFPI in the test dataset. Sensitivity was lower in lung cancers that overlapped with blind spots such as pulmonary apices, pulmonary hila, chest wall, heart, and sub-diaphragmatic space (0.50–0.64) compared with those in non-overlapped locations (0.87). The dice coefficient for the 159 malignant lesions was on average 0.52. The DL-based model was able to detect lung cancers on chest radiographs, with low mFPI.

scholarly journals Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images

2020 ◽  
pp. bjophthalmol-2020-317825
Author(s):  
Yonghao Li ◽  
Weibo Feng ◽  
Xiujuan Zhao ◽  
Bingqian Liu ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
High Myopia ◽  
Large Scale ◽  
Learning System ◽  
Youden Index ◽  
Optical Coherence ◽  
Test Dataset ◽  
Independent Test ◽  
Independent Test Dataset

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

scholarly journals Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mu Sook Lee ◽  
Yong Soo Kim ◽  
Minki Kim ◽  
Muhammad Usman ◽  
Shi Sub Byon ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Absolute Error ◽  
Training Dataset ◽  
Computer Aided Detection ◽  
Test Dataset ◽  
Cardiothoracic Ratio ◽  
Computer Aided ◽  
Chest X Ray ◽  
Public Datasets

AbstractWe examined the feasibility of explainable computer-aided detection of cardiomegaly in routine clinical practice using segmentation-based methods. Overall, 793 retrospectively acquired posterior–anterior (PA) chest X-ray images (CXRs) of 793 patients were used to train deep learning (DL) models for lung and heart segmentation. The training dataset included PA CXRs from two public datasets and in-house PA CXRs. Two fully automated segmentation-based methods using state-of-the-art DL models for lung and heart segmentation were developed. The diagnostic performance was assessed and the reliability of the automatic cardiothoracic ratio (CTR) calculation was determined using the mean absolute error and paired t-test. The effects of thoracic pathological conditions on performance were assessed using subgroup analysis. One thousand PA CXRs of 1000 patients (480 men, 520 women; mean age 63 ± 23 years) were included. The CTR values derived from the DL models and diagnostic performance exhibited excellent agreement with reference standards for the whole test dataset. Performance of segmentation-based methods differed based on thoracic conditions. When tested using CXRs with lesions obscuring heart borders, the performance was lower than that for other thoracic pathological findings. Thus, segmentation-based methods using DL could detect cardiomegaly; however, the feasibility of computer-aided detection of cardiomegaly without human intervention was limited.

scholarly journals Deep Learning Based Lung Cancer Detection and Classification

2020 ◽  
Vol 994 ◽  
pp. 012026
Author(s):  
N Kalaivani ◽  
N Manimaran ◽  
Dr. S Sophia ◽  
D D Devi
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Lung Cancer Detection

scholarly journals NIMG-08. PREDICTION OF LOWER-GRADE GLIOMA MOLECULAR SUBTYPES USING DEEP LEARNING

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii148-ii148
Author(s):  
Yoshihiro Muragaki ◽  
Yutaka Matsui ◽  
Takashi Maruyama ◽  
Masayuki Nitta ◽  
Taiichi Saito ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Molecular Subtype ◽  
Learning Model ◽  
Group Classification ◽  
Training Dataset ◽  
Lower Grade ◽  
Test Dataset ◽  
Ct Data ◽  
Deep Learning Model

Abstract INTRODUCTION It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagnose this preoperatively. METHODS A deep learning model was developed to predict the 3-group molecular subtype using multimodal data including magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). The performance was evaluated using leave-one-out cross validation with a dataset containing information from 217 LGG patients. RESULTS The model performed best when the dataset contained MRI, PET, and CT data. The model could predict the molecular subtype with an accuracy of 96.6% for the training dataset and 68.7% for the test dataset. The model achieved test accuracies of 58.5%, 60.4%, and 59.4% when the dataset contained only MRI, MRI and PET, and MRI and CT data, respectively. The conventional method used to predict mutations in the isocitrate dehydrogenase (IDH) gene and the codeletion of chromosome arms 1p and 19q (1p/19q) sequentially had an overall accuracy of 65.9%. This is 2.8 percent point lower than the proposed method, which predicts the 3-group molecular subtype directly. CONCLUSIONS AND FUTURE PERSPECTIVE A deep learning model was developed to diagnose the molecular subtype preoperatively based on multi-modality data in order to predict the 3-group classification directly. Cross-validation showed that the proposed model had an overall accuracy of 68.7% for the test dataset. This is the first model to double the expected value for a 3-group classification problem, when predicting the LGG molecular subtype. We plan to apply the techniques of heat map and/or segmentation for an increase in prediction accuracy.

scholarly journals Tobacco Smoke–Induced Lung Cancer in Animals—A Challenge to Toxicology (?)

2007 ◽  
Vol 26 (4) ◽  
pp. 339-344 ◽  
Author(s):  
Hanspeter Witschi
Keyword(s):  
Lung Cancer ◽  
Tobacco Smoke ◽  
Lung Tumor ◽  
Distant Metastases ◽  
Female Rats ◽  
Chemopreventive Agents ◽  
Lung Cancers ◽  
Malignant Lesions ◽  
Product Modification ◽  
Rats And Mice

Tobacco smoke is a known human carcinogen that primarily produces malignant lesions in the respiratory tract, although it also affects multiple other sites. A reliable and practical animal model of tobacco smoke–induced lung cancer would be helpful for in studies of product modification and chemoprevention. Over the years, many attempts to reproduce lung cancer in experimental animals exposed to tobacco smoke have been made, most often with negative or only marginally positive results. In hamsters, malignant lesions have been produced in the larynx, but not in the deeper lung. Female rats and female B6C3F1 mice, when exposed over lifetime to tobacco smoke, develop tumors in the nasal passages and also in the lung. Contrary to what is seen in human lung cancers, most rodent tumors are located peripherally and only about half of them show frank malignant features. Distant metastases are extremely rare. Male and female strain A mice exposed to 5 months to tobacco smoke and then kept for another 4 months in air respond to tobacco smoke with increased lung tumor multiplicities. However, the increase over background levels is comparatively small, making it difficult to detect significant differences when the effects of chemopreventive agents are evaluated. On the other hand, biomarkers of exposure and of effect as well as evaluation of putative carcinogenic mechanisms in rats and mice exposed to tobacco smoke allow detection of early events and their modification by different smoke types or chemopreventive agents. The challenge will be to make such data broadly acceptable and accepted in lieu of having to do more and more long term studies involving larger and larger number of animals.

scholarly journals Research on Feature Extracted Method for Flutter Test Based on EMD and CNN

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hua Zheng ◽  
Zhenglong Wu ◽  
Shiqiang Duan ◽  
Jiangtao Zhou
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Wind Tunnel ◽  
Signal Analysis ◽  
Learning Algorithm ◽  
Structural Information ◽  
Structural Response ◽  
Training Dataset ◽  
Measured Signal ◽  
Test Dataset

Due to the inevitable deviations between the results of theoretical calculations and physical experiments, flutter tests and flutter signal analysis often play significant roles in designing the aeroelasticity of a new aircraft. The measured structural response from aeroelastic models in both wind tunnel tests and real fight flutter tests contain an abundance of structural information, but traditional methods tend to have limited ability to extract features of concern. Inspired by deep learning concepts, a novel feature extraction method for flutter signal analysis was established in this study by combining the convolutional neural network (CNN) with empirical mode decomposition (EMD). It is widely hypothesized that when flutter occurs, the measured structural signals are harmonic or divergent in the time domain, and that the flutter modal (1) is singular and (2) its energy increases significantly in the frequency domain. A measured-signal feature extraction and flutter criterion framework was constructed accordingly. The measured signals from a wind tunnel test were manually labeled “flutter” and “no-flutter” as the foundational dataset for the deep learning algorithm. After the normalized preprocessing, the intrinsic mode functions (IMFs) of the flutter test signals are obtained by the EMD method. The IMFs are then reshaped to make them the suitable size to be input to the CNN. The CNN parameters are optimized though the training dataset, and the trained model is validated through the test dataset (i.e., cross-validation). The accuracy rate of the proposed method reached 100% on the test dataset. The training model appears to effectively distinguish whether or not the structural response signal contains flutter. The combination of EMD and CNN provides effective feature extraction of time series signals in flutter test data. This research explores the connection between structural response signals and flutter from the perspective of artificial intelligence. The method allows for real-time, online prediction with low computational complexity.

scholarly journals Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2424 ◽  
Author(s):  
Md Atiqur Rahman Ahad ◽  
Thanh Trung Ngo ◽  
Anindya Das Antar ◽  
Masud Ahmed ◽  
Tahera Hossain ◽  
...  
Keyword(s):  
Deep Learning ◽  
Age Estimation ◽  
Prediction Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Training Dataset ◽  
Wearable Sensor ◽  
Age And Gender ◽  
Test Dataset ◽  
And Gender

Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network.

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