An Efficient Tissue Segmentation of Neonatal Brain Magnetic Resonance Imaging

Udayakumar, E.; Yogeshwaran, K.; Ramesh, C.

doi:10.5958/0974-360x.2019.00499.2

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, the algorithm used here is convolutional artificial intelligence algorithm. This algorithm checks the given input image with the data in the dataset, and when both the images were matched, it will display the type of the skin disease [ 21 ].…”

Section: System Designmentioning

confidence: 99%

An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors

Srihari²,

S³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Skin disease is the major health problem around the world. The diagnosis of skin disease remains a challenge to dermatologist profession particularly in the detection, evaluation, and management. Health data are very large and complex due to this processing of data using traditional data processing techniques is very difficult. In this paper, to ease the complexity while processing the inputs, we use multilayered perceptron with backpropagation neural networks (MLP-BPNN). The image is collected from the devices that contain nanotechnology sensors, which is the state-of-art in the proposed model. The nanotechnology sensors sense the skin for its chemical, physical, and biological conditions with better detection specificity, sensitivity, and multiplexing ability to acquire the image for optimal classification. The MLP-BPNN technique is used to envisage the future result of disease type effectively. By using the above MLP-BPNN technique, it is easy to predict the skin diseases such as melanoma, nevus, psoriasis, and seborrheic keratosis.

show abstract

Section: System Designmentioning

confidence: 99%

An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors

Srihari²,

S³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The low PSNR regard exhibit that the two pictures are uncorrelated. The entropy esteem got for the proposed calculation is appeared in Figure 7, whose worth is which exhibit the viability proposed calculation sequestered from everything the subtleties of the first picture (Yogeshwaran, 2019).…”

Section: Similarity Analysismentioning

confidence: 82%

An Innovative Approach to Solve Healthcare Issues Using Big Data Image Analytics

Ramésh¹,

Udayakumar²,

Srihari³

et al. 2021

International Journal of Big Data and Analytics in Healthcare

View full text Add to dashboard Cite

The increasing adoption of transmission of medical images through internet in healthcare has led to several security threats to patient medical information. Permitting quiet data to be in peril may prompt hopeless harm, ethically and truly to the patient. Accordingly, it is important to take measures to forestall illicit access and altering of clinical pictures. This requests reception of security components to guarantee three fundamental security administrations – classification, content-based legitimacy, and trustworthiness of clinical pictures traded in telemedicine applications. Right now, inside created symmetric key cryptographic capacities are utilized. Pictorial model-based perceptual image hash is used to provide content-based authentication for malicious tampering detection and localization. The presentation of the projected algorithm has been evaluated using performance metrics such as PSNR, normalized correlation, entropy, and histogram analysis, and the simulation results show that the security services have been achieved effectively.

show abstract

Impact of biomaterials in lung tumor classification and segmentation using Machine learning healthcare

Udayakumar¹,

Kandasamy²,

Chellappan³

et al. 2021

Materials Today: Proceedings

View full text Add to dashboard Cite

An Efficient Tissue Segmentation of Neonatal Brain Magnetic Resonance Imaging

Cited by 3 publications

References 0 publications

An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors

An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors

An Innovative Approach to Solve Healthcare Issues Using Big Data Image Analytics

Impact of biomaterials in lung tumor classification and segmentation using Machine learning healthcare

Contact Info

Product

Resources

About