A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction

Anitha, K.; Radhika, S.; Kavitha, C.; Lai, Wen-Chen; Srividhya, S.; Naresh, K.

doi:10.3390/biomedicines10102438

Cited by 6 publications

(3 citation statements)

References 52 publications

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“…Likewise, using the EOG data of 22 subjects, [ 9 ] obtained a high correlation coefficient with an average vigilance reference of up to 0.75 for driver fatigue detection. The indexing technique takes LBP while taking into account data from nearby pixels and is noise-resistant [ 51 , 52 ]. The methods used in this experiment helped obtain an 86.7% accuracy in driver fatigue detection using electrooculography (EOG) compared to earlier experiments ( Table 2 ).…”

Section: Discussionmentioning

confidence: 99%

An Electro-Oculogram (EOG) Sensor’s Ability to Detect Driver Hypovigilance Using Machine Learning

Murugan

Sivakumar

Kavitha

et al. 2023

Sensors

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Driving safely is crucial to avoid death, injuries, or financial losses that can be sustained in an accident. Thus, a driver’s physical state should be monitored to prevent accidents, rather than vehicle-based or behavioral measurements, and provide reliable information in this regard. Electrocardiography (ECG), electroencephalography (EEG), electrooculography (EOG), and surface electromyography (sEMG) signals are used to monitor a driver’s physical state during a drive. The purpose of this study was to detect driver hypovigilance (drowsiness, fatigue, as well as visual and cognitive inattention) using signals collected from 10 drivers while they were driving. EOG signals from the driver were preprocessed to remove noise, and 17 features were extracted. ANOVA (analysis of variance) was used to select statistically significant features that were then loaded into a machine learning algorithm. We then reduced the features by using principal component analysis (PCA) and trained three classifiers: support vector machine (SVM), k-nearest neighbor (KNN), and ensemble. A maximum accuracy of 98.7% was obtained for the classification of normal and cognitive classes under the category of two-class detection. Upon considering hypovigilance states as five-class, a maximum accuracy of 90.9% was achieved. In this case, the number of detection classes increased, resulting in a reduction in the accuracy of detecting more driver states. However, with the possibility of incorrect identification and the presence of issues, the ensemble classifier’s performance produced an enhanced accuracy when compared to others.

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Section: Discussionmentioning

confidence: 99%

An Electro-Oculogram (EOG) Sensor’s Ability to Detect Driver Hypovigilance Using Machine Learning

Murugan

Sivakumar

Kavitha

et al. 2023

Sensors

Self Cite

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“…One of them is the Medical Image Retrieval (MIR) System, which can act as an effective classifier and locator for medical records with aspect to specific image analysis, diagnosis or relevant treatment method. With the support of Local Binary Pattern together with the local data analysis component, the classification and locating accuracy of this MIR system could reach approximately 99.86% within a fairly short period of time [4]. This would save the doctors a huge amount of time and vigor during the process of similar disease case diagnosis and therefore improve efficiency and accuracy.…”

Section: Medical Image Retrieval System Assisting Diagnosismentioning

confidence: 97%

Application of deep-learning based computer vision in medical image analysis

Zuo

2024

ACE

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The analyzing process of medical image has always been a crucial part for disease detection, diagnosis, surgery assisting and drug delivery. With the outbreak of population worldwide and the constant emergence of all types of complicated diseases, doctors nowadays are facing tremendous workloads and having difficulty in making precise diagnoses or performing more assured operations. Many experts have been striving to develop a wide range of techniques based on deep learning to ease doctors burden and improve patients recovery process or survival chances. This general review paper will mainly focus on providing readers with a brief understanding and knowledge about deep-learning based computer vision applied in modern medical image analysis. Several academic journals or books related to computer vision applications in medical image analysis were selected for review. Despite the advantages and convenience of this technique, this review finds out that potential obstacles still exist and can be overcome or amended in the future.

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“…Feature extraction methods can extract the hidden features of irregular patterns within images and improve retrieval performance. These methods extract Local Binary Pattern (LBP) features [16][17][18][19], Scale-Invariant Feature Transform (SIFT) features [20][21][22], as well as colour and shape features [23,24] to conduct retrieval of images with irregular patterns. Distance-based similarity metrics (e.g., Manhattan, Jaccard, Euclidean, cosine, etc.)…”

Section: Introductionmentioning

confidence: 99%

Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets

Zhang,

Cosma,

Bugby

et al. 2023

J. Imaging

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Image retrieval is the process of searching and retrieving images from a datastore based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or healthcare images by extracting features from the images, such as deep features, colour-based features, shape-based features, and local features. This has applications across a spectrum of industries, including fault inspection, disease diagnosis, and maintenance prediction. This paper proposes an image retrieval framework to search for images containing similar irregular patterns by extracting a set of morphological features (DefChars) from images. The datasets employed in this paper contain wind turbine blade images with defects, chest computerised tomography scans with COVID-19 infections, heatsink images with defects, and lake ice images. The proposed framework was evaluated with different feature extraction methods (DefChars, resized raw image, local binary pattern, and scale-invariant feature transforms) and distance metrics to determine the most efficient parameters in terms of retrieval performance across datasets. The retrieval results show that the proposed framework using the DefChars and the Manhattan distance metric achieves a mean average precision of 80% and a low standard deviation of ±0.09 across classes of irregular patterns, outperforming alternative feature–metric combinations across all datasets. Our proposed ImR framework performed better (by 8.71%) than Super Global, a state-of-the-art deep-learning-based image retrieval approach across all datasets.

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A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction

Cited by 6 publications

References 52 publications

An Electro-Oculogram (EOG) Sensor’s Ability to Detect Driver Hypovigilance Using Machine Learning

An Electro-Oculogram (EOG) Sensor’s Ability to Detect Driver Hypovigilance Using Machine Learning

Application of deep-learning based computer vision in medical image analysis

Efficient Retrieval of Images with Irregular Patterns Using Morphological Image Analysis: Applications to Industrial and Healthcare Datasets

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