Model based smooth super-twisting control of cancer chemotherapy treatment.

Chemotherapy is one of the most efficient methods for treating cancer patients. Chemotherapy aims to eliminate cancer cells as thoroughly as possible. Delivering medications to patients' bodies through various methods, either oral or intravenous is part of the chemotherapy process. Different cell-kill hypotheses take into account the interactions of the expansion of the tumor volume, external drugs, and the rate of their eradication. For the control of drug usage and tumor volume, a model based smooth super-twisting control (MBSSTC) is proposed in this paper. Firstly, three nonlinear cell-kill mathematical models are considered in this work, including the log-kill, Norton-Simon, and Emax hypotheses subject to parametric uncertainties and exogenous perturbations. In accordance with clinical recommendations, the tumor volume follows a predefined trajectory after chemotherapy. Secondly, the MBSSTC is applied for the three cell-kill models to attain accurate trajectory tracking even in the presence of uncertainties and disturbances. Compared to conventional super-twisting control (STC), the non-smooth term is introduced in the proposed control to enhance the anti-disturbance capability. Finally, simulation comparisons are performed across the proposed MBSSTC, conventional STC, and proportional-integral (PI) control methods to show the effectiveness and merits of our designed control method.

Enhanced cluster detection and noise reduction for geospatial time series data of COVID-19.

Spatial-temporal analysis of the COVID-19 cases is critical to find its transmitting behaviour and to detect the possible emerging clusters. Poisson's prospective space-time analysis has been successfully implemented for cluster detection of geospatial time series data. However, its accuracy, number of clusters, and processing time are still a major problem for detecting small-sized clusters. The aim of this research is to improve the accuracy of cluster detection of COVID-19 at the county level in the U.S.A. by detecting small-sized clusters and reducing the noisy data. The proposed system consists of the Poisson prospective space-time analysis along with Enhanced cluster detection and noise reduction algorithm (ECDeNR) to improve the number of clusters and decrease the processing time. The results of accuracy, processing time, number of clusters, and relative risk are obtained by using different COVID-19 datasets in SaTScan. The proposed system increases the average number of clusters by 7 and the average relative risk by 9.19. Also, it provides a cluster detection accuracy of 91.35% against the current accuracy of 83.32%. It also gives a processing time of 5.69 minutes against the current processing time of 7.36 minutes on average. The proposed system focuses on improving the accuracy, number of clusters, and relative risk and reducing the processing time of the cluster detection by using ECDeNR algorithm. This study solves the issues of detecting the small-sized clusters at the early stage and enhances the overall cluster detection accuracy while decreasing the processing time.

Fitness dependent optimizer with neural networks for COVID-19 patients.

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://github.com/Tarik4Rashid4/covid19models.

Kurdish Handwritten character recognition using deep learning techniques.

Handwriting recognition is regarded as a dynamic and inspiring topic in the exploration of pattern recognition and image processing. It has many applications including a blind reading aid, computerized reading, and processing for paper documents, making any handwritten document searchable and converting it into structural text form. High accuracy rates have been achieved by this technology when recognizing handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. However, there is not such a system for recognizing Kurdish handwriting. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic/Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive database has been created for handwritten Kurdish characters which contain more than 40 thousand images. The created database has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system the experimental results show an acceptable recognition level. The testing results reported an 83% accuracy rate, and training accuracy reported a 96% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and comparable to the similar to other languages handwriting recognition systems.

Harmony search: Current studies and uses on healthcare systems.

One of the popular metaheuristic search algorithms is Harmony Search (HS). It has been verified that HS can find solutions to optimization problems due to its balanced exploratory and convergence behavior and its simple and flexible structure. This capability makes the algorithm preferable to be applied in several real-world applications in various fields, including healthcare systems, different engineering fields, and computer science. The popularity of HS urges us to provide a comprehensive survey of the literature on HS and its variants on health systems, analyze its strengths and weaknesses, and suggest future research directions. In this review paper, the current studies and uses of harmony search are studied in four main domains. (i) The variants of HS, including its modifications and hybridization. (ii) Summary of the previous review works. (iii) Applications of HS in healthcare systems. (iv) And finally, an operational framework is proposed for the applications of HS in healthcare systems. The main contribution of this review is intended to provide a thorough examination of HS in healthcare systems while also serving as a valuable resource for prospective scholars who want to investigate or implement this method.

An enhanced algorithm for improving real-time video transmission for tele-training education.

Tele-training in surgical education has not been effectively implemented. There is a stringent need for a high transmission rate, reliability, throughput, and reduced distortion for high-quality video transmission in the real-time network. This work aims to propose a system that improves video quality during real-time surgical tele-training. The proposed approach aims to minimise the video frame's total distortion, ensuring better flow rate allocation and enhancing the video frames' reliability. The proposed system consists of a proposed algorithm for Enhancing Video Quality, Distorting Minimization, Bandwidth efficiency, and Reliability Maximization called (EVQDMBRM) algorithm. The proposed algorithm reduces the video frame's total distortion. In addition, it enhances the video quality in a real-time network by dynamically allocating the flow rate at the video source and maximizing the transmission reliability of the video frames. The result shows that the proposed EVQDMBRM algorithm improves the video quality with the minimized total distortion. Therefore, it improves the Peak Signal to Noise Ratio (PSNR) average by 51.13 dB against 47.28 dB in the existing systems. Furthermore, it reduces the video frames processing time average by 58.2 milliseconds (ms) against 76.1, and the end-to-end delay average by 114.57 ms against 133.58 ms comparing to the traditional methods. The proposed system concentrates on minimizing video distortion and improving the surgical video transmission quality by using an EVQDMBRM algorithm. It provides the mechanism to allocate the video rate at the source dynamically. Besides that, it minimizes the packet loss ratio and probing status, which estimates the available bandwidth.

An extensive dataset of handwritten central Kurdish isolated characters.

To collect the handwritten format of separate Kurdish characters, each character has been printed on a grid of 14 × 9 of A4 paper. Each paper is filled with only one printed character so that the volunteers know what character should be written in each paper. Then each paper has been scanned, spliced, and cropped with a macro in photoshop to make sure the same process is applied for all characters. The grids of the characters have been filled mainly by volunteers of students from multiple universities in Erbil.

A novel solution of using mixed reality in bowel and oral and maxillofacial surgical telepresence: 3D mean value cloning algorithm.

BACKGROUND AND AIM: Most of the mixed reality models used in the surgical telepresence are suffering from the discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts. METHODOLOGY: The proposed system enhanced mean-value cloning algorithm that helps to maintain the spatial-temporal consistency of the final composite video. The enhanced algorithm includes the three-dimensional mean-value coordinates and improvised mean-value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discolouration artefacts around the blending region. RESULTS: The accuracy in terms of overlay error of the proposed solution is improved from 1.01 to 0.80 mm, whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173 s from 0.211 s. The processing time and the accuracy of the proposed solution are enhanced as compared to the state-of-art solution. CONCLUSION: Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video.

A novel visualization system of using augmented reality in knee replacement surgery: Enhanced bidirectional maximum correntropy algorithm.

BACKGROUND AND AIM: Image registration and alignment are the main limitations of augmented reality (AR)-based knee replacement surgery. This research aims to decrease the registration error, eliminate outcomes that are trapped in local minima to improve the alignment problems, handle the occlusion and maximize the overlapping parts. METHODOLOGY: Markerless image registration method was used for AR-based knee replacement surgery to guide and visualize the surgical operation. While weight least square algorithm was used to enhance stereo camera-based tracking by filling border occlusion in right-to-left direction and non-border occlusion from left-to-right direction. RESULTS: This study has improved video precision to 0.57-0.61 mm alignment error. Furthermore, with the use of bidirectional points, that is, forward and backward directional cloud point, the iteration on image registration was decreased. This has led to improve the processing time as well. The processing time of video frames was improved to 7.4-11.74 frames per second. CONCLUSIONS: It seems clear that this proposed system has focused on overcoming the misalignment difficulty caused by the movement of patient and enhancing the AR visualization during knee replacement surgery. The proposed system was reliable and favourable which helps in eliminating alignment error by ascertaining the optimal rigid transformation between two cloud points and removing the outliers and non-Gaussian noise. The proposed AR system helps in accurate visualization and navigation of anatomy of knee such as femur, tibia, cartilage, blood vessels and so forth.

Object detection and recognition: using deep learning to assist the visually impaired.

BACKGROUND: Deep learning systems have improved performance of devices through more accurate object detection in a significant number of areas, for medical aid in general, and also for navigational aids for the visually impaired. Systems addressing different needs are available, and many manage effectively the detection of static obstacles. PURPOSE: This research provides a review of deep learning systems used with navigational tools for the visually Impaired and a framework for guidance for future research. METHODS: We compare current deep learning systems used with navigational tools for the visually impaired and compile a taxonomy of indispensable features for systems. RESULTS: Challenges to detection. Our taxonomy of improved navigational systems shows that it is sufficiently robust to be generally applied. CONCLUSION: This critical analysis is, to the best of our knowledge, the first of its kind and will provide a much-needed overview of the field.Implication for RehabilitationDeep learning systems can provide lost cost solutions for the visually impaired.Of these, convolutional neural networks (CNN) and fully convolutional neural networks (FCN) show great promise in terms of the development of multifunctional technology for the visually impaired (i.e., being less specific task oriented).CNN have also potential for overcoming challenges caused by moving and occluded objects.This work has also highlighted a need for greater emphasis on feedback to the visually impaired which for many technologies is limited.

A Novel Solution of Using Mixed Reality in Bowel and Oral and Maxillofacial Surgical Telepresence: 3D Mean Value Cloning algorithm.

BACKGROUND AND AIM: Most of the Mixed Reality models used in the surgical telepresence are suffering from the discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts. METHODOLOGY: The proposed system enhanced the mean value cloning algorithm that helps to maintain the spatial-temporal consistency of the final composite video. The enhanced algorithm includes the 3D mean value coordinates and improvised mean value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discoloration artefacts around the blending region RESULTS: As compared to the state of art solution, the accuracy in terms of overlay error of the proposed solution is improved from 1.01mm to 0.80mm whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173 seconds from 0.211 seconds CONCLUSION: Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video. This article is protected by copyright. All rights reserved.

A novel solution of using deep learning for left ventricle detection: Enhanced feature extraction.

BACKGROUND AND AIM: deep learning algorithms have not been successfully used for the left ventricle (LV) detection in echocardiographic images due to overfitting and vanishing gradient descent problem. This research aims to increase accuracy and improves the processing time of the left ventricle detection process by reducing the overfitting and vanishing gradient problem. METHODOLOGY: the proposed system consists of an enhanced deep convolutional neural network with an extra convolutional layer, and dropout layer to solve the problem of overfitting and vanishing gradient. Data augmentation was used for increasing the accuracy of feature extraction for left ventricle detection. RESULTS: four pathological groups of datasets were used for training and evaluation of the model: heart failure without infarction, heart failure with infarction, and hypertrophy, and healthy. The proposed model provided an accuracy of 94% in left ventricle detection for all the groups compared to the other current systems. The results showed that the processing time was reduced from 0.45 s to 0.34 s in an average. CONCLUSION: the proposed system enhances accuracy and decreases processing time in the left ventricle detection. This paper solves the issues of overfitting of the data.

A Novel Visualization System of Using Augmented Reality in Knee Replacement Surgery: Enhanced Bidirectional Maximum CorrentropyAlgorithm.

BACKGROUND AND AIM: Image registration and alignment are the main limitations of augmented reality-based knee replacement surgery. This research aims to decrease the registration error, eliminate outcomes that are trapped in local minima to improve the alignment problems, handle the occlusion and maximize the overlapping parts. METHODOLOGY: markerless image registration method was used for Augmented reality-based knee replacement surgery to guide and visualize the surgical operation. While weight least square algorithm was used to enhance stereo camera-based tracking by filling border occlusion in right to left direction and non-border occlusion from left to right direction. RESULTS: This study has improved video precision to 0.57 mm ∼ 0.61 mm alignment error. Furthermore, with the use of bidirectional points, i.e. Forwards and backwards directional cloud point, the iteration on image registration was decreased. This has led to improved the processing time as well. The processing time of video frames was improved to 7.4 ∼11.74 fps. CONCLUSIONS: It seems clear that this proposed system has focused on overcoming the misalignment difficulty caused by movement of patient and enhancing the AR visualization during knee replacement surgery. The proposed system was reliable and favourable which helps in eliminating alignment error by ascertaining the optimal rigid transformation between two cloud points and removing the outliers and non-Gaussian noise. The proposed augmented reality system helps in accurate visualization and navigation of anatomy of knee such as femur, tibia, cartilage, blood vessels, etc. This article is protected by copyright. All rights reserved.

A recent review and a taxonomy for hard and soft tissue visualization-based mixed reality.

BACKGROUND: Mixed reality (MR) visualization is gaining popularity in image-guided surgery (IGS) systems, especially for hard and soft tissue surgeries. However, a few MR systems are implemented in real time. Some factors are limiting MR technology and creating a difficulty in setting up and evaluating the MR system in real environments. Some of these factors include: the end users are not considered, the limitations in the operating room, and the medical images are not fully unified into the operating interventions. METHODOLOGY: The purpose of this article is to use Data, Visualization processing, and View (DVV) taxonomy to evaluate the current MR systems. DVV includes all the components required to be considered and validated for the MR used in hard and soft tissue surgeries. This taxonomy helps the developers and end users like researchers and surgeons to enhance MR system for the surgical field. RESULTS: We evaluated, validated, and verified the taxonomy based on system comparison, completeness, and acceptance criteria. Around 24 state-of-the-art solutions that are picked relate to MR visualization, which is then used to demonstrate and validate this taxonomy. The results showed that most of the findings are evaluated and others are validated. CONCLUSION: The DVV taxonomy acts as a great resource for MR visualization in IGS. State-of-the-art solutions are classified, evaluated, validated, and verified to elaborate the process of MR visualization during surgery. The DVV taxonomy provides the benefits to the end users and future improvements in MR.

Augmented reality for narrow area navigation in jaw surgery: Modified tracking by detection volume subtraction algorithm.

BACKGROUND AND AIM: Jaw surgery based on augmented reality (AR) still has limitations in terms of navigating narrow areas. Surgeons need to avoid nerves, vessels, and teeth in their entirety, not just root canals. Inaccurate positioning of the surgical instrument may lead to positional or navigational errors and can result in cut blood vessels, nerve channels, or root canals. This research aims to decrease the positional error during surgery and improve navigational accuracy by reducing the positional error. METHODOLOGY: The proposed 2D/3D system tracks the surgical instrument, consisting of the shaft and the cutting element, each part being assigned a different feature description. In the case of the 3D position estimation, the input vector is composed of image descriptors of the instrument and the output value consists of 3D coordinates of the cutter. RESULTS: Sample results from a jawbone-maxillary and mandibular jaw-demonstrate that the positional error is reduced. The system, thus, led to an improvement in alignment of the video accuracy by 0.25 to 0.35 mm from 0.40 to 0.55 mm and a decrease in processing time of 11 to 14 frames per second (fps) against 8 to 12 fps of existing solutions. CONCLUSION: The proposed system is focused on overlaying only on the area to be operated on. Thus, this AR-based study contributes to accuracy in navigation of the deeper anatomical corridors through increased accuracy in positioning of surgical instruments.

A novel rotation invariant and Manhattan metric-based pose refinement: Augmented reality-based oral and maxillofacial surgery.

BACKGROUND: Augmented reality (AR) is gaining attention in medicine because of the convenience and innovation that it brings to operating rooms. Furthermore, oral and maxillofacial surgery (OMS), which is one of sensitive and narrow spatial surgery, requires high accuracy in image registration and low processing time of the system. However, the current systems are suffering from image registration problems while matching two different posture images. We thus aimed to increase that overlay accuracy and decrease the processing time. METHODOLOGY: The proposed system consists of an Iterative Closest Point (ICP) algorithm, which is the combination of a rotation invariant and Manhattan error metric, to provide the best initial parameters and to decrease the computational cost by sorting high and low processing pixel images, respectively. RESULT: The study on maxillary and mandibular jaw bone demonstrates that the proposed work overlay accuracy ranges from 0.22 to 0.30 mm, and processing time ranges from 10 to 14 frames per second as opposed to the 0.23- to 0.35-mm overlay accuracy and the current 8 to 12 frames per second processing time. CONCLUSION: This research aimed to improve the visualization and fast AR system for the OMS. Thus, the proposed system achieved an improvement in overlay accuracy and processing time by implementing the Rotation Invariant and Manhattan error metric ICP algorithm.

A novel optimized initial cluster center and enhanced objective function: Medical diagnosis through classification.

Medical diagnosis through classification is often critical as the medical datasets are multilabel in nature, that is, a patient may have more than one health condition: high blood pressure, obesity, and diabetes. The aim of this article is to improve the accuracy and performance of multilabel classification using multilabel feature selection and improved overlapping clustering method. The proposed system consists of Optimized Initial Cluster Centers and Enhanced Objective Function technique to reduce the number of iterations in the clustering process thereby improving the clustering performance and to improve the clustering accuracy which will result in improving the accuracy and performance of multilabel classification. Ratios of clustering distance to class distance and execution time are used as the evaluation metric for accuracy and total execution time is used as the evaluation metric for performance. Based on the different combination with the number of labels, attributes, instances, and number of clusters, different values of accuracy and performance are obtained. The results on all 10 datasets show that the proposed technique is superior to the current technique. Furthermore, on average, the proposed technique has improved the classification accuracy by 5%-7%. Furthermore, the performance of new technique is improved by decreasing the processing time by 0.5-1 s on average. The proposed system targets on improving the accuracy and performance of the multilabel classification for medical diagnosis, which consists of multilabel feature selection and enhanced overlapping clustering technique. This study provides an acceptable range of accuracy with improved processing time, which assists the doctors in medical diagnosis (high blood pressure, obesity, and diabetes) of patients.

A novel enhanced intensity-based automatic registration: Augmented reality for visualization and localization cancer tumors.

The purpose of this study is to replace the manual process (selecting the landmarks on mesh and anchor points on the video) by Intensity-based Automatic Registration method to reach registration accuracy and low processing time. The proposed system consists of an Enhanced Intensity-based Automatic Registration (EIbAR) using Modified Zero Normalized Cross Correlation (MZNCC) algorithm. The proposed system was implemented on videos of breast cancer tumors. Results showed that the proposed algorithm-as compared to a reference-improved registration accuracy by an average of 2 mm. In addition, the proposed algorithm-as compared to a reference-reduced the number of pixel matching, thereby reducing processing time on the video by an average of 22 ms/frame. The proposed system can, thus, provide an acceptable accuracy and processing time during scene augmentation of videos, which provides a seamless use of augmented-reality for surgeons in visualizing cancer tumors.

A novel enhanced softmax loss function for brain tumour detection using deep learning.

BACKGROUND AND AIM: In deep learning, the sigmoid function is unsuccessfully used for the multiclass classification of the brain tumour due to its limit of binary classification. This study aims to increase the classification accuracy by reducing the risk of overfitting problem and supports multi-class classification. The proposed system consists of a convolutional neural network with modified softmax loss function and regularization. RESULTS: Classification accuracy for the different types of tumours and the processing time were calculated based on the probability score of the labeled data and their execution time. Different accuracy values and processing time were obtained when testing the proposed system using different samples of MRI images. The result shows that the proposed solution is better compared to the other systems. Besides, the proposed solution has higher accuracy by almost 2 % and less processing time of 40∼50 ms compared to other current solutions. CONCLUSION: The proposed system focused on classification accuracy of the different types of tumours from the 3D MRI images. This paper solves the issues of binary classification, the processing time, and the issues of overfitting of the data.