Approaches of artificial intelligence in biomedical image processing: A leading tool between computer vision &amp; biological vision

Shukla, Shruti; Lakhmani, Ashish; Agarwal, Ambuj Kumar

doi:10.1109/icacca.2016.7578900

Cited by 17 publications

(4 citation statements)

References 5 publications

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“…The ophthalmic diagnostic system is mostly dependent on eye image analysis. Human retinal images can be analyzed in a fast and non-invasive manner employing DL to extract, localize and quantify the pathological features responsible for different retinal diseases [28], [29]. Most of the recently developed DL-based retinal image analysis algorithm had been evaluated on different public retinal datasets.…”

Section: In Ophthalmologymentioning

confidence: 99%

Deep Learning in Retinal Image Segmentation and Feature Extraction: A Review

Hoque

Kipli

2021

Int. J. Onl. Eng.

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Image recognition and understanding is considered as a remarkable subfield of Artificial Intelligence (AI). In practice, retinal image data have high dimensionality leading to enormous size data. As the morphological retinal image datasets can be analyzed in an expansive and non-invasive way, AI more precisely Deep Learning (DL) methods are facilitating in developing intelligent retinal image analysis tools. The most recently developed DL technique, Convolutional Neural Network (CNN) showed remarkable efficiency in identifying, localizing, and quantifying the complex and hierarchical image features that are responsible for severe cardiovascular diseases. Different deep layered CNN architectures such as LeeNet, AlexNet, and ResNet have been developed exploiting CNN morphology. This wide variety of CNN structures can iteratively learn complex data structures of different datasets through supervised or unsupervised learning and perform exquisite analysis for feature recognition independently to diagnose threatening cardiovascular diseases. In modern ophthalmic practice, DL based automated methods are being used in retinopathy screening, grading, identifying, and quantifying the pathological features to employ further therapeutic approaches and offering a wide potentiality to get rid of ophthalmic system complexity. In this review, the recent advances of DL technologies in retinal image segmentation and feature extraction are extensively discussed. To accomplish this study the pertinent materials were extracted from different publicly available databases and online sources deploying the relevant keywords that includes retinal imaging, artificial intelligence, deep learning and retinal database. For the associated publications the reference lists of selected articles were further investigated.

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Section: In Ophthalmologymentioning

confidence: 99%

Deep Learning in Retinal Image Segmentation and Feature Extraction: A Review

Hoque

Kipli

2021

Int. J. Onl. Eng.

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“…Medical informatics with AI computation can be targeted in a recognition model to efficiently identify clinical data characteristics for health risk assessment and prevention [3][4][5]. The AI-enabled machine learning (ML) model typically consists of featuring and data training, which pre-processes the labeled samples (e.g., specific symptoms in the electrocardiogram (ECG) of arrhythmia) and explores the Sensors 2022, 22, 689 2 of 24 features to recognize clinical data [6][7][8]. Hybrid ML methods were qualified for coupling analysis of comprehensive features.…”

Section: Introductionmentioning

confidence: 99%

Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management

Hsiao

Kan

Kuo

et al. 2022

Sensors

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We established a web-based ubiquitous health management (UHM) system, “ECG4UHM”, for processing ECG signals with AI-enabled models to recognize hybrid arrhythmia patterns, including atrial premature atrial complex (APC), atrial fibrillation (AFib), ventricular premature complex (VPC), and ventricular tachycardia (VT), versus normal sinus rhythm (NSR). The analytical model coupled machine learning methods, such as multiple layer perceptron (MLP), random forest (RF), support vector machine (SVM), and naive Bayes (NB), to process the hybrid patterns of four arrhythmia symptoms for AI computation. The data pre-processing used Hilbert–Huang transform (HHT) with empirical mode decomposition to calculate ECGs’ intrinsic mode functions (IMFs). The area centroids of the IMFs’ marginal Hilbert spectrum were suggested as the HHT-based features. We engaged the MATLABTM compiler and runtime server in the ECG4UHM to build the recognition modules for driving AI computation to identify the arrhythmia symptoms. The modeling extracted the crucial data sets from the MIT-BIH arrhythmia open database. The validated models, including the premature pattern (i.e., APC–VPC) and the fibril-rapid pattern (i.e., AFib–VT) against NSR, could reach the best area under the curve (AUC) of the receiver operating characteristic (ROC) of approximately 0.99. The models for all hybrid patterns, without VPC versus AFib and VT, achieved an average accuracy of approximately 90%. With the prediction test, the respective AUCs of the NSR and APC versus the AFib, VPC, and VT were 0.94 and 0.93 for the RF and SVM on average. The average accuracy and the AUC of the MLP, RF, and SVM models for APC–VT reached the value of 0.98. The self-developed system with AI computation modeling can be the backend of the intelligent social-health system that can recognize hybrid arrhythmia patterns in the UHM and home-isolated cares.

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“…Literature Review. Currently, computer vision techniques are the recent trend, and they widely opt for several areas and applications such that the biomedical domain, aerospace engineering, construction sites, metrological department of any area and identifying botanical diseases, traffic monitoring, and many other areas [4][5][6][7][8][9]. Discuss the several types of instruments and devices that are used to identify the distress such that digital cameras that identify the cracks in the pavement and laser screening methods to identify the rutting issue [10].…”

Section: Introductionmentioning

confidence: 99%

Applications of Artificial Intelligence Enhanced Drones in Distress Pavement, Pothole Detection, and Healthcare Monitoring with Service Delivery

Wang

2022

Journal of Engineering

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Artificial Intelligence (AI) has fascinated the present study assigned to multiple areas such as distress detection on the pavement, pothole detection, and healthcare. The distress detection on pavement and roads and delivering healthcare and medical services need to be monitored through state-of-the-art technology, i.e., drone technology. Improvement in construction sites and healthcare delivery are of serious concern. Nowadays, computer vision techniques are commonly used in this area utilizing images and videos of construction sites. Due to confined data, researchers are using Unmanned Aerial Vehicles (UAVs) or Drone to get maximum information through 360° monitoring. This review article presents the useful monitoring techniques using AI-enabled drones for scholars around the world. In this comprehensive review, initially, the image acquisition equipment along with the perks and limitations has been presented. Second, the main constraints related to different computer vision techniques are highlighted for detecting distress in the pavement. Then, the possible research solution to some of the distress issues such that detection of pavement texture, cracks or potholes, joint faulting, temperature segregation, and rutting issues are predicted. Finally, the application of AI-enhanced drones in the healthcare field is elucidated which showed their significance. Moreover, in this research, the comparative image analysis of pavement and path hole detection was presented for the collection of detailed information and accurate detection. In the future, the work can also be enhanced to monitor the live pavement distress detection, especially for busy roads and highways. Moreover, an analysis to determine and reduce the costs in the healthcare sectors and organizations is required for future work.

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Approaches of artificial intelligence in biomedical image processing: A leading tool between computer vision & biological vision

Cited by 17 publications

References 5 publications

Deep Learning in Retinal Image Segmentation and Feature Extraction: A Review

Deep Learning in Retinal Image Segmentation and Feature Extraction: A Review

Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management

Applications of Artificial Intelligence Enhanced Drones in Distress Pavement, Pothole Detection, and Healthcare Monitoring with Service Delivery

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