Content Based CT Image Sign Retrieval using Fast Discrete Curvelet Transform and Deep Learning

Computed Tomography (CT) images are becoming a valuable aid in advanced lung cancer investigation. Automated segmentation of lung structures from CT images is the elemental step in many lung cancer diagnosis systems, where the malignant nodules are identified at its initial stage. Lung segmentation is a challenging task because of the variant density distribution amongst the lung region, similar densities in the pulmonary structures, and scanning protocols. This work intended to recognize the efficient region-based segmentation framework to segment lung volumes. The standard region-based approaches, such as watershed and region growing, are adapted to segment the lung region. The specified segmentation methods segment lung in the axial space and achieve promising segmentation results. The performance of the system is assessed by computing the DSC, Hausdorff distance and AVD between the automatically segmented lung volumes and the volumes manually outlined by radiological experts. The marker-controlled watershed method followed by a sequence of morphological operations segments the lung parenchyma accurately with a dice score of 92.5 and the region growing method with a dice score of 93.8 concerning the expert-traced contours. The obtained results confirm the effectiveness of the system. It is suggested that the performance of the described framework could be further enhanced by fusing shape-based features.

Section: D Lung Segmentation On Ct Images Using Region-based Methodsmentioning

confidence: 99%

3D Lung Segmentation on CT Images Using Region-Based Method

Agnes¹

2019

“…It is capable to detect and sense of electrical problems failure or defect. In other form IRT, it is also capable to assist in the medical world [20]. One of an important point, the heat produced because of resistance materials itself.…”

Section: Irt In Tracing Root Cause Of Leakage Currentmentioning

confidence: 99%

Quantitative Infrared Thermography Resolved Leakage Current Problem in Cathodic Protection System

Shariff¹

2019

Leakage current problem can happen in Cathodic Protection (CP) system installation. It could affect the performance of underground facilities such as piping, building structure, and earthing system. Worse can happen is rapid corrosion where disturbance to plant operation plus expensive maintenance cost. Occasionally, if it seems, tracing its root cause could be tedious. The traditional method called line current measurement is still valid effective. It involves isolating one by one of the affected underground structures. The recent methods are Close Interval Potential Survey and Pipeline Current Mapper were better and faster. On top of the mentioned method, there is a need to enhance further by synthesizing with the latest visual methods. Therefore, this paper describes research works on Infrared Thermography Quantitative (IRTQ) method as resolution of leakage current problem in CP system. The scope of study merely focuses on tracing the root cause of leakage current occurring at the CP system lube base oil plant. The results of experiment adherence to the hypothesis drawn. Consequently, resolve the problem and recommend betterment for the future.

“…With numerous novel approaches in medical applications using DLNs, the excessive computational cost from the conventional processes was addressed. Deep learning [20] is among the most reliable approaches in analyzing physiological signals for healthcare applications. This makes data mining strategies more useful and reliable in analyzing a large group of data like medical records.…”

Section: Introductionmentioning

confidence: 99%

A Novel Technique in Classifying Heart Diseases based on Electrocardiogram (ECG) Signals using Deep Learning and Spectrogram Image Analysis

Panganiban¹

2019

Deep learning is among the most reliable approaches in analyzing physiological signals for healthcare applications. This makes data mining strategies more useful and reliable in analyzing a large group of data like medical records. Through this technology breakthrough, the authors established a novel approach in examining electrocardiogram (ECG) records and classify them on what kind of heart disease based from its pattern using spectrogram image analysis. These records underwent data training as part of the deep learning process. Data trained are in the form of extracted ECG logs in comma-separated values (CSV) format from different patient cases. These databases are downloaded from Physionet databases, which offers free access to an extensive collection of recorded physiological data. Once done downloading, these databases were categorized per disease type. Records of diseases that were in the same category must be saved in the same folder. These CSV files passed through a preprocessing procedure through filtering CSV log files and cut each ECG reading into 10-second duration and save it as text file. These text files were converted into their spectrogram images equivalent. The converted text files to spectrogram images were still categorized per disease type. The file name of each folder containing the spectrogram images for each disease type served as the label map for the output classifier. The final layer was retrained for dataset development through the use of Google's Inception V3 model that serves as a base learning model. Fine tuning was established through setting its training rate to 0.001, training batch size to 500, a validation batch size of 200 and a total number of training steps of 500,000. These settings had achieved the optimum result of 100% validation and training accuracy each. The result of final test accuracy is 98.9% and had generated a frozen model for the classifier. Its accuracy can improve if there are more available and reliable ECG records. Using the frozen model, the same procedures were done in preparing the dataset to be used for the testing stage. The result of the classification of disease is shown through the percentage value of how likely is the input spectrogram image based from disease categories. The authors evaluated the proposed technique and obtained a significant performance on the classification of types of heart diseases based on available databases from Physionet.