scholarly journals Respiratory Diseases Detection using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 2698-2701
Author(s):  
Shubham Shitole
Keyword(s):  
Machine Learning ◽  
Ct Scan ◽  
Lung Diseases ◽  
Respiratory Diseases ◽  
Diagnostic System ◽  
Extraction Process ◽  
Image Feature ◽  
Feature Selection Technique ◽  
Preprocessing Technique ◽  
Lengthy Process

Prediction of the Respiratory diseases in the earlier stage can be very useful specially to improve the survival rate of that patient. CT scan images are used to detect various lung diseases .These CT scan reports are sent to pathologists for further process. Pathologists analyze CT scan report and predict the infected tissues which are the main cause of the particular disease. This is lengthy process and to avoid this steps and increase the accuracy of the prediction Machine learning plays an important role . The system proposes to build "Predictive Diagnostic System" of infectious lung by using the concept of image processing in conjunction with machine learning. Proposed system will detect the disease from CT scan images and use preprocessing technique that will remove the noise and disturbance in image. Feature extraction process is applied to extract the useful features of underlying image, and feature selection technique will further optimize the top ranking features. CNN algorithm is then applied to classify the images for detection of Respiratory disease. After detection of disease, report will be generated and submitted to patient.

scholarly journals Machine-Learning Based Hybrid-Feature Analysis for Liver Cancer Classification Using Fused (MR and CT) Images

2020 ◽  
Vol 10 (9) ◽  
pp. 3134 ◽  
Author(s):  
Samreen Naeem ◽  
Aqib Ali ◽  
Salman Qadri ◽  
Wali Khan Mashwani ◽  
Nasser Tairan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Cancer ◽  
Ct Scan ◽  
Region Of Interest ◽  
Hepatocellular Adenoma ◽  
Cancer Classification ◽  
Support Vector ◽  
Hybrid Features ◽  
Feature Selection Technique ◽  
Probability Of Error

The purpose of this research is to demonstrate the ability of machine-learning (ML) methods for liver cancer classification using a fused dataset of two-dimensional (2D) computed tomography (CT) scans and magnetic resonance imaging (MRI). Datasets of benign (hepatocellular adenoma, hemangioma, cyst) and malignant (hepatocellular carcinoma, hepatoblastoma, metastasis) liver cancer were acquired at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. The final dataset was generated by fusion of 1200 (100 × 6 × 2) MR and CT-scan images, 200 (100 MRI and 100 CT-scan) images size 512 × 512 for each class of cancer. The acquired dataset was preprocessed by employing the Gabor filters to reduce the noise and taking an automated region of interest (ROIs) using an Otsu thresholding-based segmentation approach. The preprocessed dataset was used to acquire 254 hybrid-feature data for each ROI, which is the combination of the histogram, wavelet, co-occurrence, and run-length features, while 10 optimized hybrid features were selected by employing (probability of error plus average correlation) feature selection technique. For classification, we deployed this optimized hybrid-feature dataset to four ML classifiers: multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and J48, using a ten fold cross-validation method. MLP showed an overall accuracy of (95.78% on MRI and 97.44% on CT). Unfortunately, the obtained results were not promising, and there were some limitations due to the different modalities of the dataset. Thereafter, a fusion of MRI and CT-scan datasets generated the fused optimized hybrid-feature dataset. The MLP has shown a promising accuracy of 99% among all the deployed classifiers.

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

2020 ◽  
Vol 16 (8) ◽  
pp. 937-945
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Detection Techniques ◽  
The Brain

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

scholarly journals Automobile tire life prediction based on image processing and machine learning technology

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110027
Author(s):  
Jianchen Zhu ◽  
Kaixin Han ◽  
Shenlong Wang
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Life Prediction ◽  
Traffic Accidents ◽  
Confusion Matrix ◽  
Small Sample ◽  
Image Feature ◽  
Engineering Practice ◽  
Learning Technology ◽  
K Nearest Neighbor

With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

scholarly journals A Personalized Machine-Learning-Enabled Method for Efficient Research in Ethnopharmacology. The Case of the Southern Balkans and the Coastal Zone of Asia Minor

2021 ◽  
Vol 11 (13) ◽  
pp. 5826
Author(s):  
Evangelos Axiotis ◽  
Andreas Kontogiannis ◽  
Eleftherios Kalpoutzakis ◽  
George Giannakopoulos
Keyword(s):  
Machine Learning ◽  
Coastal Zone ◽  
Extraction Process ◽  
Asia Minor ◽  
Efficiency And Effectiveness ◽  
Effectiveness And Efficiency ◽  
Intelligent Tools ◽  
Southern Balkans

Ethnopharmacology experts face several challenges when identifying and retrieving documents and resources related to their scientific focus. The volume of sources that need to be monitored, the variety of formats utilized, and the different quality of language use across sources present some of what we call “big data” challenges in the analysis of this data. This study aims to understand if and how experts can be supported effectively through intelligent tools in the task of ethnopharmacological literature research. To this end, we utilize a real case study of ethnopharmacology research aimed at the southern Balkans and the coastal zone of Asia Minor. Thus, we propose a methodology for more efficient research in ethnopharmacology. Our work follows an “expert–apprentice” paradigm in an automatic URL extraction process, through crawling, where the apprentice is a machine learning (ML) algorithm, utilizing a combination of active learning (AL) and reinforcement learning (RL), and the expert is the human researcher. ML-powered research improved the effectiveness and efficiency of the domain expert by 3.1 and 5.14 times, respectively, fetching a total number of 420 relevant ethnopharmacological documents in only 7 h versus an estimated 36 h of human-expert effort. Therefore, utilizing artificial intelligence (AI) tools to support the researcher can boost the efficiency and effectiveness of the identification and retrieval of appropriate documents.

scholarly journals CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 617
Author(s):  
Umer Saeed ◽  
Young-Doo Lee ◽  
Sana Ullah Jan ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Diagnostic System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Context Aware ◽  
Sensor Faults ◽  
Training Time ◽  
Low Intensity ◽  
Fault Diagnostic

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

scholarly journals Dynamic monitor of CT scan within short interval in invasive pulmonary aspergillosis for nonneutropenic patients: a retrospective analysis in two centers

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fei Chen ◽  
Yonghong Zhong ◽  
Na Li ◽  
Huijie Wang ◽  
Yanbin Tan ◽  
...  
Keyword(s):  
Ct Scan ◽  
Respiratory Diseases ◽  
Short Interval ◽  
Invasive Pulmonary Aspergillosis ◽  
Host Factors ◽  
Antifungal Treatment ◽  
Ct Scans ◽  
Pulmonary Aspergillosis ◽  
European Organization ◽  
Life Threatening

Abstract Background In nonneutropenic patients with underlying respiratory diseases (URD), invasive pulmonary aspergillosis (IPA) is a life-threatening disease. Yet establishing early diagnosis in those patients remains quite a challenge. Methods A retrospective series of nonneutropenic patients with probable or proven IPA were reviewed from January 2014 to May 2018 in Department of Respiratory Medicine of two Chinese hospitals. Those patients were suspected of IPA and underwent lung computed tomography (CT) scans twice within 5–21 days. The items required for IPA diagnosis were assessed by their host factors, mycological findings and CT scans according to the European Organization for Research and Treatment of Cancer (EORTC) and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (MSG) criteria (EORTC/MSG criteria). Results Together with the risk factors, mycological findings and nonspecific radiological signs on first CT, ten patients were suspected of IPA. With the appearance of cavities on second CT scan in the following days, all patients met the criteria of probable or possible IPA. Except one patient who refused antifungal treatment, nine patients received timely antifungal treatment and recovered well. One of the nine treated IPA cases was further confirmed by pathology, one was confirmed by biopsy. Conclusions Dynamic monitor of CT scan provided specific image evidences for IPA diagnosis. This novel finding might provide a noninvasive and efficient strategy in IPA diagnosis with URD.

Investigating the Spread of Coronavirus Disease via Edge-AI and Air Pollution Correlation

2021 ◽  
Vol 21 (4) ◽  
pp. 1-10
Author(s):  
V. Gomathy ◽  
K. Janarthanan ◽  
Fadi Al-Turjman ◽  
R. Sitharthan ◽  
M. Rajesh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Mortality Rate ◽  
Respiratory Diseases ◽  
Past Research ◽  
Viral Disease ◽  
Machine Learning Method ◽  
Learning Method ◽  
Death Rates ◽  
Using Data

Coronavirus Disease 19 (COVID-19) is a highly infectious viral disease affecting millions of people worldwide in 2020. Several studies have shown that COVID-19 results in a severe acute respiratory syndrome and may lead to death. In past research, a greater number of respiratory diseases has been caused by exposure to air pollution for long periods of time. This article investigates the spread of COVID-19 as a result of air pollution by applying linear regression in machine learning method based edge computing. The analysis in this investigation have been based on the death rates caused by COVID-19 as well as the region of death rates based on hazardous air pollution using data retrieved from the Copernicus Sentinel-5P satellite. The results obtained in the investigation prove that the mortality rate due to the spread of COVID-19 is 77% higher in areas with polluted air. This investigation also proves that COVID-19 severely affected 68% of the individuals who had been exposed to polluted air.

Detection of Roller Bearing Conditions Using Mechatronics Approach

1999 ◽  
Author(s):  
T. I. Liu ◽  
F. Ordukhani
Keyword(s):  
Neural Networks ◽  
Roller Bearing ◽  
Diagnostic System ◽  
Feature Selection Technique ◽  
Vibration Signals ◽  
Selection Technique ◽  
On Line ◽  
Line Classification ◽  
Nonlinear Pattern

Abstract An on-line monitoring and diagnostic system is needed to detect faulty bearings. In this work, by applying the feature selection technique to the data obtained from vibration signals, six indices were selected. Artificial neural networks were used for nonlinear pattern recognition. An attempt was made to distinguish between normal and defective bearings. Counterpropagation neural networks with various network sizes were trained for these tasks. The counterpropagation neural networks were able to recognize a normal from a defective bearing with the success rate between 88.3% to 100%. The best results were obtained when all the six indices were used for the on-line classification of roller bearings.

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