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Öğe A survey of artificial intelligence/machine learning-based trends for prostate cancer analysis(2024) Sailunaz, Kashfia; Beştepe, Deniz; Alhajj, Lama; Özyer, Tansel; Rokne, Jon; Alhajj, RedaDifferent types of cancer are more commonly encountered recently. This may be attributed to a variety of reasons, including heredity, changes in the living conditions (food, drinks, pollution, etc.), advancement in technology which allowed for better diagnosis of diseases, among others. Prostate one of the main types of cancers witnessed in males; it has indeed been identified as the second type cancer leading to death in males. Accordingly, it has received considerable attention from the research community where computer scientists and data analysts are closely collaborating with pathologists to develop automated techniques and tools capable of classifying and identifying cancerous cases with high accuracy. These efforts are described in the literature in a large number of research articles which makes it hard and time consuming for researchers to grasp the current state of the art. Instead, review articles form a valuable source for researchers who are interesting in coping with the developments in the field. Generally, the literature includes several survey papers on prostate cancer; each of them tackles some aspect of the domain up to the time when the survey was prepared. Hence the need for the survey described in this paper which highlights the scope of each of the previous survey papers encountered in the literature and adds upon the latest developments in the field as described in more recent papers published mainly in 2023 and 2024. The survey focuses on the main artificial intelligence and machine learning techniques for diagnosing prostate cancer based on various types of data, including MRI. The most recent techniques employed in analyzing prostate cancer data, the various types of data, the available datasets, the reported results, etc. are all covered. This will help researchers in their efforts to keep track of the recent developments in the field and to realize the challenges which need more attention along the path towards developing robust and effect decision support systems for pathologists to have higher self confidence in handling their patients.Öğe A survey of machine learning-based methods for COVID-19 medical image analysis(Springer Science and Business Media Deutschland GmbH, 2023) Sailunaz, Kashfia; Özyer, Tansel; Rokne, Jon; Alhajj, RedaThe ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches. [Figure not available: see fulltext.]Öğe An efficient approach to predict eye diseases from symptoms using machine learning and ranker-based feature selection methods(MDPI, 2023) Marouf, Ahmed Al; Mottalib, Md Mozaharul; Alhajj, Reda; Rokne, Jon; Jafarullah, OmarThe eye is generally considered to be the most important sensory organ of humans. Diseases and other degenerative conditions of the eye are therefore of great concern as they affect the function of this vital organ. With proper early diagnosis by experts and with optimal use of medicines and surgical techniques, these diseases or conditions can in many cases be either cured or greatly mitigated. Experts that perform the diagnosis are in high demand and their services are expensive, hence the appropriate identification of the cause of vision problems is either postponed or not done at all such that corrective measures are either not done or done too late. An efficient model to predict eye diseases using machine learning (ML) and ranker-based feature selection (r-FS) methods is therefore proposed which will aid in obtaining a correct diagnosis. The aim of this model is to automatically predict one or more of five common eye diseases namely, Cataracts (CT), Acute Angle-Closure Glaucoma (AACG), Primary Congenital Glaucoma (PCG), Exophthalmos or Bulging Eyes (BE) and Ocular Hypertension (OH). We have used efficient data collection methods, data annotations by professional ophthalmologists, applied five different feature selection methods, two types of data splitting techniques (train-test and stratified k-fold cross validation), and applied nine ML methods for the overall prediction approach. While applying ML methods, we have chosen suitable classic ML methods, such as Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), AdaBoost (AB), Logistic Regression (LR), k-Nearest Neighbour (k-NN), Bagging (Bg), Boosting (BS) and Support Vector Machine (SVM). We have performed a symptomatic analysis of the prominent symptoms of each of the five eye diseases. The results of the analysis and comparison between methods are shown separately. While comparing the methods, we have adopted traditional performance indices, such as accuracy, precision, sensitivity, F1-Score, etc. Finally, SVM outperformed other models obtaining the highest accuracy of 99.11% for 10-fold cross-validation and LR obtained 98.58% for the split ratio of 80:20.Öğe Approaches for early detection of glaucoma using retinal images: A performance analysis(Springer Science and Business Media Deutschland GmbH, 2020) Sarhan, Abdullah; Rokne, Jon; Alhajj, RedaSight is one of the most important senses for humans, as it allows them to see and explore their surroundings. Multiple ocular diseases damaging sight have been detected over the years such as glaucoma and diabetic retinopathy. Glaucoma is a group of diseases that can lead to blindness if left untreated. No cure for glaucoma exists apart from early detection and treatment by an ophthalmologist. Retinal images provide vital information about an eye’s health. On the basis of advancements in retinal images technology it is possible to develop systems that can analyze these images for better diagnosis. To test the efficiency of some of the developed techniques, we obtained the code for four different approaches and did a performance analysis using four public datasets. We investigated the results along with the analysis time. The outcomes of the study are approaches for glaucoma detection;behavior of glaucoma related approaches on retinal images with different ocular diseases;challenges faced when analyzing retinal images; andglaucoma risk factors.Öğe Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust(Public Library of Science, 2023) Sailunaz, Kashfia; Beştepe, Deniz; Alhajj, Sleiman; Özyer, Tansel; Rokne, Jon; Alhajj, RedaBrain cancers caused by malignant brain tumors are one of the most fatal cancer types with a low survival rate mostly due to the difficulties in early detection. Medical professionals therefore use various invasive and non-invasive methods for detecting and treating brain tumors at the earlier stages thus enabling early treatment. The main non-invasive methods for brain tumor diagnosis and assessment are brain imaging like computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI) scans. In this paper, the focus is on detection and segmentation of brain tumors from 2D and 3D brain MRIs. For this purpose, a complete automated system with a web application user interface is described which detects and segments brain tumors with more than 90% accuracy and Dice scores. The user can upload brain MRIs or can access brain images from hospital databases to check presence or absence of brain tumor, to check the existence of brain tumor from brain MRI features and to extract the tumor region precisely from the brain MRI using deep neural networks like CNN, U-Net and U-Net++. The web application also provides an option for entering feedbacks on the results of the detection and segmentation to allow healthcare professionals to add more precise information on the results that can be used to train the model for better future predictions and segmentations.Öğe Convex hull in brain tumor segmentation(Springer Science and Business Media Deutschland GmbH, 2022) Sailunaz, Kashfia; Beştepe, Deniz; Alhajj, Sleiman; Özyer, Tansel; Rokne, Jon; Alhajj, RedaTumors are the second leading cause of death. Among the tumors, brain tumors constitute one of the most complex tumor categories with a high mortality rate. Therefore, brain tumor detection and segmentation from non-invasive imaging like MRI is an important research area. Although most recent researches for brain tumor detection are focused on deep learning methods, machine learning, geometrical approaches, thresholding and hybrid models are also explored frequently. In this paper, a novel brain tumor segmentation method containing thresholding, computational geometry and heuristics is proposed. The proposed model is tested with two brain tumor datasets to show comparative results for brain tumor segmentation with thresholding, convex hull and an area heuristic. The application of different filtering on a direct convex hull model and a heuristic-based convex hull model shows that the convex area based heuristic with the convex hull approach is able to segment brain tumors more accurately than previous approaches.Öğe Creating a learning profile by using face and emotion recognition(2023) Yurtdaş, Gözde; Alhajj, Loubaba; Sailunaz, Kashfia; Özyer, Tansel; Rokne, Jon; Alhajj, RedaThe aim of this work is to employ face recognition for creating learning profiles of the analysed persons who are students in this study. Generating education profiles will help experts in the diagnosis of Attention Deficit Hyperactivity Disorder (ADHD), which is a serious problem in children. Children with ADHD often have the ability and potential to learn. However, it may be difficult to reveal their capabilities and skills. Accordingly, a suffering child may have a hard time succeeding in real life when he/she is ignored and expected to mix with other children. The unrealized gap and deficiency may lead to other problems and more complicated situation with unpredictable consequences. Thanks to the system developed in this study, and the like, which will help in diagnosing the ADHD disease, and hence suffering individuals will be able to recognize their deficiencies, understand their ability to learn and adapt when approached differently in a way which suits his/her situation. This personalized handling of infected students will be an excellent guide to advance their potential and integration within the society carefully and smoothly. The system analyzes the face of a student to inspire his/her emotional state. The reported test results demonstrate how the system works well and produces high accuracy under a variety of severe conditions such as skewed angle, less illumination, accessories etc.Öğe Glaucoma detection using image processing techniques: A literature review(Elsevier, 2019) Sarhan, Abdullah; Rokne, Jon; Alhajj, RedaThe term glaucoma refers to a group of heterogeneous diseases that cause the degeneration of retinal ganglion cells (RGCs). The degeneration of RGCs leads to two main issues: (i) structural changes to the optic nerve head as well as the nerve fiber layer, and (ii) simultaneous functional failure of the visual field. These two effects of glaucoma may lead to peripheral vision loss and, if the condition is left to progress it may eventually lead to blindness. No cure for glaucoma exists apart from early detection and treatment by optometrists and ophthalmologists. The degeneration of RGCs is normally detected from retinal images which are assessed by an expert. These retinal images also provide other vital information about the health of an eye. Thus, it is essential to develop automated techniques for extracting this information. The rapid development of digital images and computer vision techniques have increased the potential for analysis of eye health from images. This paper surveys current approaches to detect glaucoma from 2D and 3D images; both the limitations and possible future directions are highlighted. This study also describes the datasets used for retinal analysis along with existing evaluation algorithms. The main topics covered by this study may be enumerated as follows: • approaches to segment different objects from both 2D and 3D images; • approaches that may lead to encouraging results for glaucoma detection; • challenges faced by researchers; and • currently available retinal datasets and evaluation methods.Öğe Human behavior modeling for simulating evacuation of buildings during emergencies(Elsevier, 2019) Şahin, Coşkun; Rokne, Jon; Alhajj, RedaEvery year, a considerably large number of disasters occur as a result of natural events or human faults. In order to decrease the damage and casualties associated with each disaster, it is crucial to get prepared for these kind of situations. Indeed, emergency evacuation is a crucial part of this preparation. Researchers have been working on creating evacuation simulation systems over the past few decades. They are trying to model the environment, human physiology and psychology as realistic as possible to make the analysis more accurate. However, there is yet no comprehensive system which well cover the emerging situations and guarantees the avoidance of causalities, environmental and economic damage. In this paper, we propose an approach which combines a multi-agent model with fuzzy logic to smoothly and successfully handle multiple features of each individual to simulate common human and group behavior during safety egress. The developed simulation system considers situations where a crowd is blocked inside a building or a zone during a disaster. Agents capture various aspects related to humans who may be present in such a region. Each agent possesses different features to realistically simulate a human by encapsulating the psychology, sociology, mood, reaction, etc. Integrating fuzziness in the model allows for more natural capturing of human behavior during the evacuation process. Different scenarios have been tried in the conducted experiments. The outcome revealed the importance of various characteristics of the zone to be evacuated and how they could positively affect the safeness of the evacuation plans. For instance, increasing the width of an exit up to a certain limit may be very beneficial in the process based on the density of the crowd to be evacuated. Actually, the reported simulation results demonstrate the applicability and effectiveness of the proposed approach.Öğe Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust(Public Library of Science, 2022) Sailunaz, Kashfia; Beştepe, Deniz; Özyer, Tansel; Rokne, Jon; Alhajj, RedaDue to the severity and speed of spread of the ongoing Covid-19 pandemic, fast but accurate diagnosis of Covid-19 patients has become a crucial task. Achievements in this respect might enlighten future efforts for the containment of other possible pandemics. Researchers from various fields have been trying to provide novel ideas for models or systems to identify Covid-19 patients from different medical and non-medical data. AI-based researchers have also been trying to contribute to this area by mostly providing novel approaches of automated systems using convolutional neural network (CNN) and deep neural network (DNN) for Covid-19 detection and diagnosis. Due to the efficiency of deep learning (DL) and transfer learning (TL) models in classification and segmentation tasks, most of the recent AI-based researches proposed various DL and TL models for Covid-19 detection and infected region segmentation from chest medical images like X-rays or CT images. This paper describes a web-based application framework for Covid-19 lung infection detection and segmentation. The proposed framework is characterized by a feedback mechanism for self learning and tuning. It uses variations of three popular DL models, namely Mask R-CNN, UNet, and U-Net++. The models were trained, evaluated and tested using CT images of Covid patients which were collected from two different sources. The web application provide a simple user friendly interface to process the CT images from various resources using the chosen models, thresholds and other parameters to generate the decisions on detection and segmentation. The models achieve high performance scores for Dice similarity, Jaccard similarity, accuracy, loss, and precision values. The U-Net model outperformed the other models with more than 98% accuracy.Öğe Investigating the roles of micrornas / lncrnas in characterizing breast cancer subtypes and prognosis(2023) Pek, Reyhan Zeynep; Zavalsız, Muhammed Talha; Serdar, Melis; Alhajj, Lama; Alhajj, Sleiman; Sailunaz, Kashfia; Özyer, Tansel; Rokne, Jon; Alhajj, RedaMolecular subtyping is a method of separating tumor clusters in a cancer type with common features according to molecular data and classification models. Genome datasets are taken from many different people and some genetic material, more precisely genetic markers, are obtained to predict the presence of a disease. In addition, breast cancer occurs due to mutation or modification observed in cells. miRNAs and lncRNAs take participation in cell cycle, regulation, and even chromatic inhibition of cell. For example, miRNAs function in cell cycle regulation as the degradation of mRNAs. Therefore, the aim of this work is to investigate the roles of miRNAs and lncRNAs in prognosis and characterizing the subtypes of Breast Cancer.Öğe KoExPubMed: A tool for effective and customized knowledge extraction from PubMed(2023) Sailunaz, Kashfia; Jurca, Gabi; Beştepe, Deniz; Karatay, Büşra; Alhajj, Lama; Özyer, Tansel; Rokne, Jon; Alhajj, RedaAn exponential growth in the literature in general and the medical literature in particular raises a need for effective intelligent analysis strategies and tools to provide valuable insights to researchers about the current evolving literature. While existing applications provide more specific approaches to the problem, such as focusing on particular genome or protein information, in this paper, the proposed application provides effective and detailed analysis of PubMed. The developed tool, named KoExPubMed, follows a more generalized and holistic way by taking into consideration different types of information such as authors, countries, genes, and the interactions between them. The developed application consists of four main components; (1) keyword search and ID extraction, (2) PubMed article information and abstract retrieval, (3) country and address extraction, and (4) gene information extraction. In addition to the fundamental components, the tool provides a variety of visualization options for showing the extracted information and the related associations, including line charts for densities and countries, chord charts for collaborations of authors, network graphs for the genes mentioned together, bubble charts for gene frequencies, etc. By addressing the need for a generalized data mining tool, we propose a comprehensive application which is capable of employing data mining and machine learning techniques to extract from PubMed knowledge valuable to researchers and practitioners who are interested in closely investigating the achievements of others.Öğe NetDriller-V3: A powerful social network analysis tool(Institute of Electrical and Electronics Engineers Inc., 2023) Afra, Salim; Özyer, Tansel; Rokne, Jon; Alhajj, RedaThe development in technology has led to the generation of huge amounts of data from various sources, including biological data, social networking data, etc. Accordingly, social network analysis has received considerable attention with the availability of more raw datasets which could be realized using a network structure. Most of the datasets can be represented as a social network which is a graph consisting of actors having relationships. Many tools exist for social network analysis inspired to extract knowledge from the networks. NetDriller has been developed as a social network extraction, manipulation and analysis tool to cover the lack that exists in other tools. It is capable of constructing social networks from raw data by employing a variety of data mining and machine learning techniques. In this paper, we describe an extend version of NetDriller, which has some new essential functions, including social network construction using data collection from Twitter, DBLP and IEEE. We also added (1) a new chart for viewing the network property and metrics, and (2) new graph manipulation techniques using GUI to keep the tool up to date with the huge volume of networks and the different types of raw data available on the web.Öğe Twitter analysis in emergency management: recent research and trends(2024) Arvandi, Alireza; Rokne, Jon; Alhajj, RedaA disaster is an unexpected event with negative consequences for individuals and societies. Typically it is interfering with a community’s or society’s ability to function. Disasters can be man-made events, accidental events, or a natural catastrophes. These events create emergencies that require rapid responses. Timely information about the emergencies has to be obtained so that the responses can aid in dealing with the emergencies. One source of timely information about disasters is provided by social media postings which often provide on-site information about a disaster. An ideally suited social media tool for disaster information is Twitter due to the sort message format. This format enables the rapid composition of short messages by entities close to a disaster describing the nature of the disaster. The contents of the messages can then be used to guide emergency responses. The aim of this paper is to review the research on the usage of Twitter for emergency management that has been published so far. There are three steps required when using messages for information about disasters. The first step is to collect the data contained in the messages. Then the data has to be preprocessed for unification of format, duplication etc. Finally relevant information has to be extracted. These steps are considered in this paper reviewing the use of Twitter for emergency management. Papers using Twitter published within the past 5 years have been included.
