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Your search keyword '"Iqbal, Uzair"' showing total 21 results
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21 results on '"Iqbal, Uzair"'

11. A hybrid mask RCNN-based tool to localize dental cavities from real-time mixed photographic images

Author

Rashid, Umer, Javid, Aiman, Khan, Abdur Rehman, Liu, Leo, Ahmed, Adeel, Khalid, Osman, Saleem, Khalid, Meraj, Shaista, Iqbal, Uzair, Nawaz, Raheel, Rashid, Umer, Javid, Aiman, Khan, Abdur Rehman, Liu, Leo, Ahmed, Adeel, Khalid, Osman, Saleem, Khalid, Meraj, Shaista, Iqbal, Uzair, and Nawaz, Raheel

Abstract

Nearly 3.5 billion humans have oral health issues, including dental caries, which requires dentist-patient exposure in oral examinations. The automated approaches identify and locate carious regions from dental images by localizing and processing either colored photographs or X-ray images taken via specialized dental photography cameras. The dentists’ interpretation of carious regions is difficult since the detected regions are masked using solid coloring and limited to a particular dental image type. The software-based automated tools to localize caries from dental images taken via ordinary cameras requires further investigation. This research provided a mixed dataset of dental photographic (colored or X-ray) images, instantiated a deep learning approach to enhance the existing dental image carious regions’ localization procedure, and implemented a full-fledged tool to present carious regions via simple dental images automatically. The instantiation mainly exploits the mixed dataset of dental images (colored photographs or X-rays) collected from multiple sources and pre-trained hybrid Mask RCNN to localize dental carious regions. The evaluations performed by the dentists showed that the correctness of annotated datasets is up to 96%, and the accuracy of the proposed system is between 78% and 92%. Moreover, the system achieved the overall satisfaction level of dentists above 80%.

Published
2022

18. Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Author

Mastoi, Qurat-ul-ain, Wah, Teh Ying, Gopal Raj, Ram, and Iqbal, Uzair

Subjects
ComputingMethodologies_PATTERNRECOGNITION, Article Subject
Abstract

Coronary artery disease (CAD) is the most dangerous heart disease which may lead to sudden cardiac death. However, CAD diagnoses are quite expensive and time-consuming procedures which a patient need to go through. The aim of our paper is to present a unique review of state-of-the-art methods up to 2017 for automatic CAD classification. The protocol of review methods is identifying best methods and classifier for CAD identification. The study proposes two workflows based on two parameter sets for instances A and B. It is necessary to follow the proper procedure, for future evaluation process of automatic diagnosis of CAD. The initial two stages of the parameter set A workflow are preprocessing and feature extraction. Subsequently, stages (feature selection and classification) are same for both workflows. In literature, the SVM classifier represents a promising approach for CAD classification. Moreover, the limitation leads to extract proper features from noninvasive signals.

Published
2018
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21. A Deterministic Approach for Finding the T Onset Parameter of Flatten T Wave in ECG.

Author

IQBAL, UZAIR, YING-WAH TEH, REHMAN, MUHAMMAD HABIB UR, and MASTOI, QURAT-UL-AIN

Subjects
ELECTROCARDIOGRAPHY, SIGNAL processing, CARDIOLOGISTS, PARAMETER estimation, FEATURE extraction
Abstract

Identification of the exact nature of flatten T wave in ECG signal is Classification of normal and abnonnal T wave episodes especially, regarding Flatten T wave in electrocardiography (ECG) signal is still a complex phenomenon for cardiologists. Identification of Flatten T wave depends on four parameters; Time duration (Tdur), T onset (Ton), T offset (Toff) and T peak (Tpk) values which play a vital role to identify the exact nature of Flatten T wave. The proposed approach is used to extract the Ton value of Flatten T wave with the detection of R peaks and RR intervals. The proposed approach is applied to ten different subjects of Flatten. It is divided into three distinct phases. Firstly, Flatten signals are segmented by lead wise. Secondly, noise filtration is done to identify the peak values and removal of low-frequency components. A third phase computes the R peak values and RR intervals with the help proposed algorithm. By using the R peak value as a fiducial point and considering the last interval of RR interval instead of complete T wave altemans detection algorithm (TWA) for determination the Ton parameter of Flatten T wave. The experimental evaluation manifests that efficiency factors are high in rate during the operational investigations (closest to the range of 100%). These efficiency factors have been discussed in the context of accuracy, sensitivity, prediction and error rate. These operation efficiency factors will play a benchmark role in future for calculation the others parameters of Flatten T wave. [ABSTRACT FROM AUTHOR]

Published
2019
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