Abstract: Background: Early diagnosis of liver cancer may increase life expectancy. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) play a vital role in diagnosing liver cancer. Together, both modalities offer significant individual and specific diagnosis data to physicians; however, they lack the integration of both types of information. To address this concern, a registration process has to be utilized for the purpose, as multimodal details are crucial in providing the physician with complete information. Objective: The aim was to present a model of CT-MRI registration used to diagnose liver cancer, specifically for improving the quality of the liver images and provide all the required information for earlier detection of the tumors. This method should concurrently address the issues of imaging procedures for liver cancer to fasten the detection of the tumor from both modalities. Methods: In this work, a registration scheme for fusing the CT and MRI liver images is studied. A feature point-based method with normalized cross-correlation has been utilized to aid in the diagnosis of liver cancer and provide multimodal information to physicians. Data on ten patients from an online database were obtained. For each dataset, three planar views from both modalities were interpolated and registered using feature point-based methods. The registration of algorithms was carried out by MATLAB (vR2019b, Mathworks, Natick, USA) on an Intel (R) Core (TM) i5-5200U CPU @ 2.20 GHz computer. The accuracy of the registered image is being validated qualitatively and quantitatively. Results: The results show that an accurate registration is obtained with minimal distance errors by which CT and MRI were accurately registered based on the validation of the experts. The RMSE ranges from 0.02 to 1.01 for translation, which is equivalent in magnitude to approximately 0 to 5 pixels for CT and registered image resolution. Conclusion: The CT-MRI registration scheme can provide complementary information on liver cancer to physicians, thus improving the diagnosis and treatment planning process.

Many apartment dwellers dream of growing plants in their own home. But,they are limited with the space and time for planting a fresh healthy plant in their home. However, it doesn’t have to be a dream anymore as we are introducing a portable mini greenhouse to accommodate this community. A greenhouse is used to grow plants such as flowers, vegetables and fruits under the controlled climatic conditions for efficient production, forms an important part of the agriculture and horticulture sectors. A mini greenhouse also has such quality with the extra advantages as it comes in compact sizes which fit in the apartment lifestyles. The mini greenhouse proposed by this project uses an automatic monitoring system to monitor the temperature, humidity and water supply for the plant of the mini greenhouse. This will accommodate the user to monitor their greenhouse remotely. This automated greenhouse monitoring system also collects the data from the greenhouse measured by the sensors and the results will be shown on the LCD screen for easy monitoring purposes. It is easy to install yet comes with low cost monitoring system. In addition, it will provide more convenient and low maintenance system for the mini greenhouse user who desires to plant at home but has a very limited fund and space in their living area. Keywords: Mini Greenhouse System, Automated Green House, Arduino, Sensors

The segmentation of the left ventricle (LV) is one of the fundamental procedures that must be performed to obtain quantitative measures of the heart, such as its volume, area, and ejection fraction. In clinical practice, the delineation of LV is still often conducted semi-automatically, leaving it open to operator subjectivity. The automatic LV segmentation from echocardiography images is a challenging task due to poorly defined boundaries and operator dependency. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, the well-known state-of-the-art segmentation models still lack in terms of accuracy and speed. This study aims to develop a single-stage lightweight segmentation model that precisely and rapidly segments the LV from 2D echocardiography images. In this research, a backbone network is used to acquire both low-level and high-level features. Two parallel blocks, known as the spatial feature unit and the channel feature unit, are employed for the enhancement and improvement of these features. The refined features are merged by an integrated unit to segment the LV. The performance of the model and the time taken to segment the LV are compared to other established segmentation models, DeepLab, FCN, and Mask RCNN. The model achieved the highest values of the dice similarity index (0.9446), intersection over union (0.8445), and accuracy (0.9742). The evaluation metrics and processing time demonstrate that the proposed model not only provides superior quantitative results but also trains and segments the LV in less time, indicating its improved performance over competing segmentation models.

In the late December of 2019, a novel coronavirus was discovered in Wuhan, China. In March 2020, WHO announced this epidemic had become a global pandemic and that the novel coronavirus may be mild to most people. However, some people may experience a severe illness that results in hospitalization or maybe death. COVID-19 classification remains challenging due to the ambiguity and similarity with other known respiratory diseases such as SARS, MERS, and other viral pneumonia. The typical symptoms of COVID-19 are fever, cough, chills, shortness of breath, loss of smell and taste, headache, sore throat, chest pains, confusion, and diarrhoea. This research paper suggests the concept of transfer learning using the deterministic algorithm in all binary classification models and evaluates the performance of various CNN architectures. The datasets of 746 CT images of COVID-19 and non-COVID-19 were divided for training, validation, and testing. Various augmentation techniques were applied to increase the number of datasets except for testing images. The images were then pretrained using CNN to obtain a binary class. ResNeXt101 and ResNet152 have the best F1 score of 0.978 and 0.938, whereas GoogleNet has an F1 score of 0.762. ResNeXt101 and ResNet152 have an accuracy of 97.81% and 93.80%. ResNeXt101, DenseNet201, and ResNet152 have 95.71%, 93.81%, and 90% sensitivity, whereas ResNeXt101, ResNet101, and ResNet152 have 100%, 99.58%, and 98.33 specificity, respectively.

Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning architectures, specifically convolutional neural networks have profoundly become the primary choice for the assessment of cardiac medical images. The left ventricle is a vital part of the cardiovascular system where the boundary and size perform a significant role in the evaluation of cardiac function. Due to automatic segmentation and good promising results, the left ventricle segmentation using deep learning has attracted a lot of attention. This article presents a critical review of deep learning methods used for the left ventricle segmentation from frequently used imaging modalities including magnetic resonance images, ultrasound, and computer tomography. This study also demonstrates the details of the network architecture, software, and hardware used for training along with publicly available cardiac image datasets and self-prepared dataset details incorporated. The summary of the evaluation matrices with results used by different researchers is also presented in this study. Finally, all this information is summarized and comprehended in order to assist the readers to understand the motivation and methodology of various deep learning models, as well as exploring potential solutions to future challenges in LV segmentation.

This paper presents a comprehensive review and proposes prospective research directions for the establishment of a patient-centered hospital in Malaysia that aligns with the principles of maqasid syariah. The integration of maqasid syariah, which encompasses the higher objectives and goals of Islamic law, with patient-centered care aims to create a healthcare environment that prioritizes the well-being and needs of patients while adhering to Islamic ethical principles. The review encompasses an analysis of the key principles of maqasid syariah, including the preservation of life, human dignity, justice, and holistic well-being. These research directions encompass various aspects, including healthcare facility design, communication strategies, equitable access to care, ethical considerations, and the integration of spirituality in healthcare. The outcomes of this research are expected to contribute to the advancement of healthcare practices in Malaysia by integrating Islamic values and patient-centered care principles in developing a framework that can be done in future. The proposed patient-centered hospital will not only provide high-quality care but also ensure the preservation of life, uphold human dignity, promote justice, and address the holistic wellbeing of patients.

Atherosclerotic plaque deposit in the carotid artery is used as an early estimate to identify the presence of cardiovascular diseases. Ultrasound images of the carotid artery are used to provide the extent of stenosis by examining the intima-media thickness and plaque diameter. A total of 361 images were classified using machine learning and deep learning approaches to recognize whether the person is symptomatic or asymptomatic. CART decision tree, random forest, and logistic regression machine learning algorithms, convolutional neural network (CNN), Mobilenet, and Capsulenet deep learning algorithms were applied in 202 normal images and 159 images with carotid plaque. Random forest provided a competitive accuracy of 91.41% and Capsulenet transfer learning approach gave 96.7% accuracy in classifying the carotid artery ultrasound image database.

The purpose of this paper is to review the new management policy in medical imaging of the Covid-19 post-pandemic transition. This paper discussed the Standard Operating Procedure (SOP) introduced by the Ministry of Health (MoH) Malaysia to prevent and control intrahospital transmissions of Covid-19. A conceptual framework is proposed to highlight the key areas in the national SOP for preventing Covid-19 intrahospital transmissions in the radiology department. The key areas were classified into four categories: planned requests (patient appointments), (ii) open-access management (walk-in patient workflow and the triage system), (iii) direct contact (during radiology procedures), and (iv) exit policy and disinfection (post imaging conduct). The paper ends with a summary of diagnostic imaging classifications based on chest radiographs (CXR) and Computed Tomography (CT) images of suspected and confirmed Covid-19 patients. The Covid-19 SOP for the radiology department by the MoH was found to retain most of the patient quarantine and isolation guidelines by the Centre for Disease Control and Prevention (CDC) and incorporated several international policies on patient triage and disinfection of radiological equipment. The majority of the SOP is also sustained, like the SOP during the pandemic, except for the SOP that has been proven to be insignificant by recent research. The Covid-19 SOP for the radiology department plays an important role in reducing the intrahospital spread of Covid-19, with some areas needing improvement. Health workers in the radiology department should continue implementing the Covid-19 SOP and increase their knowledge in identifying Covid-19 signs on radiographic images to help safeguard themselves and the patients from intrahospital transmissions.

The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations’ expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment’s preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment’s preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.

Image registration approaches based on standard information criteria were widely employed, showing promising results in the registration of medical monomodal images. Feature-based registering is an effective clinical application technique since computational costs can be reduced significantly. The following four steps are generally used for most registration methods: detection of features, extraction of features, matching features and transformation determination. The accuracy of the registration procedure is dependent on matching a feature and detecting control points (CP). Thus, this paper supports this feature for monomodal registration of magnetic resonance imaging (MRI). MRI is gold-standard imaging to detect prostate cancer progression as it provides better information for soft tissue visualization. In this study, registration of MRI images taken from different time frames is developed to ease the physician in integrating the information about the evolution of the tumour. The accuracy of the registration process depends on matching features and CP detection by calculating the iterative closest point (ICP). The prostate volumes are calculated, and the result shows minimal errors. This registration method has been applied in coronal, sagittal and axial views from five patient datasets. The accuracy of automatic registration results is 0.1mm (axial), 1.1mm (coronal) and 0.2mm (sagittal). These accuracies are comparable to gold-standard manual registration by the experts. There was no significant difference between the automatically and manually registered MRI monomodal. Thus, the proposed method enables the physician to diagnose prostate cancer as it can provide important information about the disease progression and decide the necessary therapies regarding the patient’s condition

Background This study presents a registration system that integrates preoperative cardiac Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) volume data with 2D Ultrasound (US) images of the aortic valve. The registration process aims to combine three different imaging modalities (US-CT-MRI) to improve the accuracy of diagnosing aortic valve disorders and provide surgical guidance during the implantation and replacement of the transcatheter aortic valve. Methods The registration framework involves two key components: temporal synchronization and spatial registration. Temporal synchronization allows the identification of frames in the CT and MRI volume that correspond to the same cardiac phase as the US time-series data. For spatial registration, an intensity-based normalized mutual information method combined with a pattern search optimization algorithm is used to produce interpolated cardiac CT and MRI images that align with the US image. Results The accuracy of the trimodality registration method is evaluated using the Dice similarity coefficient. The obtained coefficients are 0.92±0.05 and 0.92±0.04 for comparisons between US-CT and US-MRI, respectively, in short-axis "Mercedes Benz" sign views. The Hausdorff distance, which measures the dissimilarity between two sets of points, was found to be 1.49±0.20 and 1.49±0.19 for both US-CT and US-MRI pairings, respectively. Notably, these values are comparable to the precision achieved when an expert manually registers each image. Conclusions The proposed registration technique demonstrates excellent accuracy in enhancing image-guided systems for aortic valve surgical guidance. It shows promise in the context of Transcatheter Aortic Valve Implantation (TAVI) and Transcatheter Aortic Valve Replacement (TAVR) procedures. The successful integration of US-CT-MRI imaging modalities enables better diagnosis and surgical planning for aortic valve disorders, potentially leading to improved patient outcomes in these procedures.