Wyniki wyszukiwania - BazTech

1

Artificial Intelligence and Machine Learning in Emergency Medicine

Tang Kenneth Jian Wei, Ang Candice Ke En, Constantinides Theodoros, Rajinikanth V., Acharya U. Rajendra, Cheong Kang Hao

Biocybernetics and Biomedical Engineering

|

2021

|

Vol. 41, no. 1

156--172

EN

The advent of Artificial Intelligence (AI) has resulted in development of novel applications in a multitude of fields, such as in Medicine, to aid medical professionals in clinical diagnosis. Specifically, the field of Emergency Medicine has been of immense interest to researchers, with vast untapped potential for AI solutions to improve operational efficiencies and quality of healthcare. Aside from primary healthcare facilities, the Emergency Department serves as the first line of contact to patients, who often present with varying and undifferentiated symptoms. Several challenges faced by clinicians and patients alike, such as waiting times and diagnostic dilemmas, present opportunities for application of AI solutions. In this paper, we aim to summarise the applications of AI in the field of Emergency Medicine by reviewing recent developments in Emergency Department operations and in the clinical management of patients.

2

An automated skin melanoma detection system with melanoma-index based on entropy features

Cheong Kang Hao, Tang Kenneth Jian Wei, Zhao Xinxing, Koh Joel En Wei, Faust Oliver, Gururajan Raj, Ciaccio Edward J., Rajinikanth V., Acharya U. Rajendra

Biocybernetics and Biomedical Engineering

|

2021

|

Vol. 41, no. 3

997--1012

EN

Skin melanoma is a potentially life-threatening cancer. Once it has metastasized, it may cause severe disability and death. Therefore, early diagnosis is important to improve the conditions and outcomes for patients. The disease can be diagnosed based on Digital-Dermoscopy (DD) images. In this study, we propose an original and novel Automated Skin-Melanoma Detection (ASMD) system with Melanoma-Index (MI). The system incorporates image pre-processing, Bi-dimensional Empirical Mode Decomposition (BEMD), image texture enhancement, entropy and energy feature mining, as well as binary classification. The system design has been guided by feature ranking, with Student’s t-test and other statistical methods used for quality assessment. The proposed ASMD was employed to examine 600 benign and 600 DD malignant images from benchmark databases. Our classification performance assessment indicates that the combination of Support Vector Machine (SVM) and Radial Basis Function (RBF) offers a classification accuracy of greater than 97.50%. Motivated by these classification results, we also formulated a clinically relevant MI using the dominant entropy features. Our proposed index can assist dermatologists to track multiple information-bearing features, thereby increasing the confidence with which a diagnosis is given.

3

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Dey Nilanjan, Rajinikanth V., Shi Fuqian, Tavares João Manuel R.S., Moraru Luminita, Arvind Karthik K., Lin Hong, Kamalanand K., Emmanuel C.

Biocybernetics and Biomedical Engineering

|

2019

|

Vol. 39, no. 3

843--856

EN

Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance- Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation.

4

Setpoint weighted PID controller tuning for unstable system using heuristic algorithm

Rajinikanth V., Latha K.

Archives of Control Sciences

|

2012

|

Vol. 22, no. 4

481-505

EN

Most of the real time chemical process loops are unstable in nature and designing a suitable controller for such systems are difficult than open loop stable processes. In this work, an attempt is made with a two degree of freedom setpoint weighted PID controller tuning procedure for a class of unstable systems using the recent heuristic algorithms such as Particle Swarm Optimization and Bacterial Foraging Optimization. The problem considered in this study is to aptly tune the controller in order to enhance the overall closed loop performance. A novel objective function proposed in this study is used to monitor the heuristic algorithms in order to get the optimal controller parameters like Kp, Ki, Kd, and alpha with minimized iteration number. The proposed method is validated with a simulation study and this helps to accomplish enhanced system performance such as smooth reference tracking, satisfactory disturbance rejection, and error minimization for a class of unstable systems.