Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

Dash, Sonali; Verma, Sahil; Kavita, Kavita; Bevinakoppa, Savitri; Woźniak, Marcin; Shafi, Jana; Ijaz, Muhammad Fazal

doi:10.3390/sym14020194

Cited by 64 publications

(18 citation statements)

References 55 publications

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“…DBN learning is quicker than DNN due to the inclusion of RBM. The RBMs are stacked DBM with unguided connections across the levels [48][49][50][51][52][53].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

Haseena¹,

Subbaraj²,

Madavan³

et al. 2022

Computational and Mathematical Methods in Medicine

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In recent times, nutrition recommendation system has gained increasing attention due to their need for healthy living. Current studies on the food domain deal with a recommendation system that focuses on independent users and their health problems but lack nutritional advice to individual users. The proposed system is developed to suggest nutritional food to people based on age and gender predicted from their face image. The designed methodology preprocesses the input image before performing feature extraction using the deep convolution neural network (DCNN) strategy. This network extracts D -dimensional characteristics from the source face image, followed by the feature selection strategy. The face’s distinctive and identifiable traits are chosen utilizing a hybrid particle swarm optimization (HPSO) technique. Support vector machine (SVM) is used to classify a person’s age and gender. The nutrition recommendation system relies on the age and gender classes. The proposed system is evaluated using classification rate, precision, and recall using Adience dataset and UTKface dataset, and real-world images exhibit excellent performance by achieving good prediction results and computation time.

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“…DBN learning is quicker than DNN due to the inclusion of RBM. The RBMs are stacked DBM with unguided connections across the levels [48][49][50][51][52][53].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

Haseena¹,

Subbaraj²,

Madavan³

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Cao et al [ 28 ] used the YOLO model for breast lesion parts for detection and obtained a high recognition accuracy. Dash et al [ 29 ] proposed a joint model of fast-guided and matched filters, which improved the ability to extract vessel features by subsampling the filtered input image. The method achieved very high accuracy on the DRIVE and CHASE_DB1 benchmark datasets.…”

Section: Related Workmentioning

confidence: 99%

Object Detection in Medical Images Based on Hierarchical Transformer and Mask Mechanism

Shou

Meng

et al. 2022

Computational Intelligence and Neuroscience

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The object detection task in the medical field is challenging in terms of classification and regression. Due to its crucial applications in computer-aided diagnosis and computer-aided detection techniques, an increasing number of researchers are transferring the object detection techniques to the medical field. However, in existing work on object detection, researchers do not consider the low resolution of medical images, the high amount of noise, and the small size of the objects to be detected. Based on this, this paper proposes a new algorithmic model called the MS Transformer, where a self-supervised learning approach is used to perform a random mask on the input image to reconstruct the input features, learn a richer feature vector, and filter out excessive noise. To focus the model on the small objects that are being detected, the hierarchical transformer model is introduced in this paper, and a sliding window with a local self-attention mechanism is used to give a higher attention score to the small objects to be detected. Finally, a single-stage object detection framework is used to predict the sequence of sets at the location of the bounding box and the class of objects to be detected. On the DeepLesion and BCDD benchmark dataset, the model proposed in this paper achieves better performance improvement on multiple evaluation metric categories.

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“…In AI-enabled big data analytics, AI methods execute on this diverse data through some machine learning (ML) algorithms. This process examines a large amount of data (i.e., healthcare data) to uncover hidden patterns and other useful information from it [8]. This is further helpful for the prediction of some phenomena.…”

Section: E Ai-enabled Big Data Analytics Phasementioning

confidence: 99%

ASCP-IoMT: AI-Enabled Lightweight Secure Communication Protocol for Internet of Medical Things

et al. 2022

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The Internet of Medical Things (IoMT) is a unification of smart healthcare devices, tools, and software, which connect various patients and other users to the healthcare information system through the networking technology. It further reduces unnecessary hospital visits and the burden on healthcare systems by connecting the patients to their healthcare experts (i.e., doctors) and allows secure transmission of healthcare data over an insecure channel (e.g., the Internet). Since Artificial Intelligence (AI) has a great impact on the performance and usability of an information system, it is important to include its modules in a healthcare information system, which will be very helpful for the prediction of some phenomena, such as chances of getting a heart attack and possibility of a tumor, from the collected and analysed healthcare data. To mitigate these issues, in this paper, a new AI-enabled lightweight, secure communication scheme for an IoMT environment has been designed and named as ASCP-IoMT, in short. The security analysis of ASCP-IoMT is performed in different ways, such as an informal way and a formal way (through the random oracle model). ASCP-IoMT performs better than other similar schemes and provides superior security with extra functionality features as compared those for the existing state of art solutions. A practical implementation of ASCP-IoMT is also performed in order to measure its impact on various network performance parameters. The end to end delay values of ASCP-IoMT are 0.01587, 0.07440 and 0.17097 seconds and the throughput values of ASCP-IoMT are 5.05, 10.88 and 16.41 bps under the different considered cases, respectively. For AI-based Big data analytics phase, the values of computation time (seconds) for decision tree, support vector machine (SVM), and logistic regression are measured as 0.19, 0.23, and 0.27, respectively. Moreover, the different values of accuracy for decision tree, SVM and logistic regression are 84.24%, 87.57%, and 85.20%, respectively. From these values, it is clear that decision tree method requires less time than the other considered techniques, whereas accuracy is high in case of SVM.

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Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

Cited by 64 publications

References 55 publications

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

Object Detection in Medical Images Based on Hierarchical Transformer and Mask Mechanism

ASCP-IoMT: AI-Enabled Lightweight Secure Communication Protocol for Internet of Medical Things

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