No abstract

In our proposed method, wavelet transform is inked first to excerpt features from MR image, and then to lessen the dimensions of features the principle component analysis has been applied. The lessen features have been given as input to a Support Vector Machine (SVM) that uses kernel function for transforming the lower dimension feature space to upper dimension feature space. An approach of K-fold-stratified-cross validation has been taken care of to improve the generalization of kernel support vector machines. The proposed method has used different kernels and observed that the Gaussian Radial Basis function represents the accuracy result as 90.3% which is the highest.

No abstract

A technique for recognising and labeling malignant brain tissues according to the types of tumours present is known as tumour classification. Magnetic resonance imaging (MRI) can be used in clinical settings to both diagnose and treat gliomas. For clinical diagnosis and treatment planning, the ability to correctly diagnose a brain tumour from MRI images is essential. Manual classification, however, is not feasible in a timely manner due to the enormous volume of data produced by MRI. For classification and segmentation, it is required to employ automated algorithms. However, the numerous spatial and anatomical differences present in brain tumours make MRI image segmentation challenging. We have created a unique CNN architecture for classifying three different types of brain cancers. The new network was demonstrated to be more straightforward than earlier networks using MRI images with contrast-enhanced T1 pictures. Two 10-fold cross-validation techniques, two datasets, and an evaluation of the network's performance were used. A piece of upgraded picture information is used to assess the transferability of the network as part of the subject-cross-validation process. When used for record-wise cross-validation, this method of tenfold cross-validation ground set has an accuracy rate of 92.65 percent. Radiologists who operate in the ground of medical diagnostics may find the newly proposed CNN architecture to be a helpful decision-support tool due to its new transferability capability and speedy execution..

Emerging technologies including such Internet of Things (IoT) and blockchain contribute significantly to the improvement of health services. The purpose of this chapter is to achieve and democratize services through the provision of medical care as a service. The result was the development of medical gadgets integrating healthcare sensors. It links medical equipment like the temperature controller to the cloud environment of medical doctors and staff. This study introduced the combination of IoT and Blockchain as a secure platform to reduce the scarcity of nurses. Blockchain was employed for storing and validating patient information in the proposed operating framework. A significant reduction in nursing gaps for large-scale patients has been shown. All technological specifications have been given to allow the prototyping execution of these suggested medical services simply adaptable. This article deals with Blockchain technology inclusion in Remote Medical Monitoring Devices Internet of Things (IoT) security. The document provides the advantages of Blockchain based safety methods and practical barriers in remote health monitoring via IoT devices. The study also examines several cryptographic methods appropriate for IoT implementation.