The Promises of AI on Radiomics for Medical Research and Its Implementation Framework | Chapter 1 | Research Highlights in Science and Technology Vol. 4

 Medical research has currently been considerably benefited from the radiomics approach. Using radiomics allows for noninvasive belief of the pathology of tumor metastases before the collection of data consistently obtained subsequently surgery, which supports an early prediction of the effect. This study sheds light on the implementation of radiomics in healing research. This paper outlined the main parts of the radiomic framework, which involve image purchase, data collection and stowing, image separation, feature extraction, feature selection, and dossier analysis. Moreover, it expressed the implementation steps for applying machine intelligence and deep neural network algorithms to radiomics. As a result of utilizing deep neural networks, promising results have happened obtained. As a result of this work, scientists should be aware of all mechanics issues in the radiomics framework that concede possibility affect the extraction of radiomics.

Author(s) Details:

Fatma Alshohoumi,
Department of Computer Science, College of Science, Sultan Qaboos University (SQU), Alkouth, Oman.

Abdullah Al-Hamdani,
Department of Computer Science, College of Science, Sultan Qaboos University (SQU), Alkouth, Oman.

Please see the link here: https://stm.bookpi.org/RHST-V4/article/view/10907

Secure and Authenticate Indexing of Medical Images Using Region of Non-Interest | Chapter 7 | Recent Developments in Medicine and Medical Research Vol. 6

 An image database stores medical images created by several modalities. Medical photos should be transferred with the patient's information without modifying the image data, and these databases should be safely moved across the public network with control viability. Watermarking medical images is an effective approach for enhancing security. Validation of medical data is also important because it is used for further deductions and references. The investigation of medical picture watermarking strategies for safeguarding and authenticating medical data is the focus of the work. It also includes an estimate for the use of the water stamping process. The goal of this chapter is to go through Secured Indexing of Lung CT Images (SILI), which is a secure method of indexing lung CT images with patient data. The sender's logo information is used for authentication, and the secret key is used to insert the watermark into the host picture. The watermark is embedded in the lung CT image's Region of Noninterest (RONI). The lung tissue from a CT scan image is segmented to identify RONI.

Author(S) Details

I. Jasmine Selvakumari Jeya
Department of Information Technology, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India.

S. Lokesh
Department of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, Tamil Nadu, India.

M. Ramya Devi
Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India.

View Book:- https://stm.bookpi.org/RDMMR-V6/article/view/4513