Recognition of diabetic retinopathy and macular edema using deep learning.

Diabetic retinopathy (DR) and diabetic macular edema (DME) are both serious eye conditions associated with diabetes and if left untreated, and they can lead to permanent blindness. Traditional methods for screening these conditions rely on manual image analysis by experts, which can be time-consuming and costly due to the scarcity of such experts. To overcome the aforementioned challenges, we present the Modified CornerNet approach with DenseNet-100. This system aims to localize and classify lesions associated with DR and DME. To train our model, we first generate annotations for input samples. These annotations likely include information about the location and type of lesions within the retinal images. DenseNet-100 is a deep CNN used for feature extraction, and CornerNet is a one-stage object detection model. CornerNet is known for its ability to accurately localize small objects, which makes it suitable for detecting lesions in retinal images. We assessed our technique on two challenging datasets, EyePACS and IDRiD. These datasets contain a diverse range of retinal images, which is important to estimate the performance of our model. Further, the proposed model is also tested in the cross-corpus scenario on two challenging datasets named APTOS-2019 and Diaretdb1 to assess the generalizability of our system. According to the accomplished analysis, our method outperformed the latest approaches in terms of both qualitative and quantitative results. The ability to effectively localize small abnormalities and handle over-fitted challenges is highlighted as a key strength of the suggested framework which can assist the practitioners in the timely recognition of such eye ailments.

A Circular Box-Based Deep Learning Model for the Identification of Signet Ring Cells from Histopathological Images.

Signet ring cell (SRC) carcinoma is a particularly serious type of cancer that is a leading cause of death all over the world. SRC carcinoma has a more deceptive onset than other carcinomas and is mostly encountered in its later stages. Thus, the recognition of SRCs at their initial stages is a challenge because of different variants and sizes and illumination changes. The recognition process of SRCs at their early stages is costly because of the requirement for medical experts. A timely diagnosis is important because the level of the disease determines the severity, cure, and survival rate of victims. To tackle the current challenges, a deep learning (DL)-based methodology is proposed in this paper, i.e., custom CircleNet with ResNet-34 for SRC recognition and classification. We chose this method because of the circular shapes of SRCs and achieved better performance due to the CircleNet method. We utilized a challenging dataset for experimentation and performed augmentation to increase the dataset samples. The experiments were conducted using 35,000 images and attained 96.40% accuracy. We performed a comparative analysis and confirmed that our method outperforms the other methods.

CXray-EffDet: Chest Disease Detection and Classification from X-ray Images Using the EfficientDet Model.

The competence of machine learning approaches to carry out clinical expertise tasks has recently gained a lot of attention, particularly in the field of medical-imaging examination. Among the most frequently used clinical-imaging modalities in the healthcare profession is chest radiography, which calls for prompt reporting of the existence of potential anomalies and illness diagnostics in images. Automated frameworks for the recognition of chest abnormalities employing X-rays are being introduced in health departments. However, the reliable detection and classification of particular illnesses in chest X-ray samples is still a complicated issue because of the complex structure of radiographs, e.g., the large exposure dynamic range. Moreover, the incidence of various image artifacts and extensive inter- and intra-category resemblances further increases the difficulty of chest disease recognition procedures. The aim of this study was to resolve these existing problems. We propose a deep learning (DL) approach to the detection of chest abnormalities with the X-ray modality using the EfficientDet (CXray-EffDet) model. More clearly, we employed the EfficientNet-B0-based EfficientDet-D0 model to compute a reliable set of sample features and accomplish the detection and classification task by categorizing eight categories of chest abnormalities using X-ray images. The effective feature computation power of the CXray-EffDet model enhances the power of chest abnormality recognition due to its high recall rate, and it presents a lightweight and computationally robust approach. A large test of the model employing a standard database from the National Institutes of Health (NIH) was conducted to demonstrate the chest disease localization and categorization performance of the CXray-EffDet model. We attained an AUC score of 0.9080, along with an IOU of 0.834, which clearly determines the competency of the introduced model.

Impact role stress on turnover intentions of Pakistan's healthcare workers: Mediating and moderating role of organizational cynicism and self-efficacy.

Nurses make up most of the global healthcare system, thus justifying their significance in the respective industry. The healthcare profession is amongst the very few careers that are attributed to a very high level of stress and imbalanced work-life equilibrium. Over past decades, the said nature of work has been observed to coerce countless nurses to leave their respective organizations. Considering this, the current study primarily evaluates the impact of role stressors on the turnover intentions of nurses employed in the healthcare industry of Pakistan. Secondly, the study examines the mediatory role of organizational cynicism between the role stressors and turnover intentions to explain the escalating trend of nurses intending to leave the industry. Lastly, it assesses the moderating role of self-efficacy between the organizational cynicism and the intended turnover of a nurse, to gauge the amount of variation self-efficacy can cause to mitigate the negative attitudes of employed individuals. The current study was conducted in the twin metropolitans of Pakistan i.e., Rawalpindi and Islamabad. A total of 394 responses were statistically evaluated using SmartPLS 3.0. The results of the current study indicated a significant impact of role stressors on the turnover intentions of nurses. Also, organizational cynicism was observed as a significant mediator between role stressors and turnover intentions. Further, self-efficacy as well was observed as a significant moderator between organizational cynicism and the intent of healthcare workers to leave the organization. The present study addressed the conceptual research gap by exploring the direct cumulative effect of role stress (role ambiguity, role conflict, role stressors, work-family conflict) in developing the intents of professionals to quit their employing organizations, the mediatory role of organizational cynicism, and the moderation effect of self-efficacy between the undertaken variables. While in terms of abridging the contextual research gap, the current study evaluated the proposed research model within the healthcare sector of Pakistan. The findings of the current study commended the management personnel of the healthcare industry to provide the nurses with healthy professional environments to work in, as well as catering to their professional and personal expectations to a better extent. Hence, increasing the ownership of an individual depicted towards his/her employing organization.

A robust deep learning approach for tomato plant leaf disease localization and classification.

Tomato plants' disease detection and classification at the earliest stage can save the farmers from expensive crop sprays and can assist in increasing the food quantity. Although, extensive work has been presented by the researcher for the tomato plant disease classification, however, the timely localization and identification of various tomato leaf diseases is a complex job as a consequence of the huge similarity among the healthy and affected portion of plant leaves. Furthermore, the low contrast information between the background and foreground of the suspected sample has further complicated the plant leaf disease detection process. To deal with the aforementioned challenges, we have presented a robust deep learning (DL)-based approach namely ResNet-34-based Faster-RCNN for tomato plant leaf disease classification. The proposed method includes three basic steps. Firstly, we generate the annotations of the suspected images to specify the region of interest (RoI). In the next step, we have introduced ResNet-34 along with Convolutional Block Attention Module (CBAM) as a feature extractor module of Faster-RCNN to extract the deep key points. Finally, the calculated features are utilized for the Faster-RCNN model training to locate and categorize the numerous tomato plant leaf anomalies. We tested the presented work on an accessible standard database, the PlantVillage Kaggle dataset. More specifically, we have obtained the mAP and accuracy values of 0.981, and 99.97% respectively along with the test time of 0.23 s. Both qualitative and quantitative results confirm that the presented solution is robust to the detection of plant leaf disease and can replace the manual systems. Moreover, the proposed method shows a low-cost solution to tomato leaf disease classification which is robust to several image transformations like the variations in the size, color, and orientation of the leaf diseased portion. Furthermore, the framework can locate the affected plant leaves under the occurrence of blurring, noise, chrominance, and brightness variations. We have confirmed through the reported results that our approach is robust to several tomato leaf diseases classification under the varying image capturing conditions. In the future, we plan to extend our approach to apply it to other parts of plants as well.

Efficient-ECGNet framework for COVID-19 classification and correlation prediction with the cardio disease through electrocardiogram medical imaging.

In the last 2 years, we have witnessed multiple waves of coronavirus that affected millions of people around the globe. The proper cure for COVID-19 has not been diagnosed as vaccinated people also got infected with this disease. Precise and timely detection of COVID-19 can save human lives and protect them from complicated treatment procedures. Researchers have employed several medical imaging modalities like CT-Scan and X-ray for COVID-19 detection, however, little concentration is invested in the ECG imaging analysis. ECGs are quickly available image modality in comparison to CT-Scan and X-ray, therefore, we use them for diagnosing COVID-19. Efficient and effective detection of COVID-19 from the ECG signal is a complex and time-taking task, as researchers usually convert them into numeric values before applying any method which ultimately increases the computational burden. In this work, we tried to overcome these challenges by directly employing the ECG images in a deep-learning (DL)-based approach. More specifically, we introduce an Efficient-ECGNet method that presents an improved version of the EfficientNetV2-B4 model with additional dense layers and is capable of accurately classifying the ECG images into healthy, COVID-19, myocardial infarction (MI), abnormal heartbeats (AHB), and patients with Previous History of Myocardial Infarction (PMI) classes. Moreover, we introduce a module to measure the similarity of COVID-19-affected ECG images with the rest of the diseases. To the best of our knowledge, this is the first effort to approximate the correlation of COVID-19 patients with those having any previous or current history of cardio or respiratory disease. Further, we generate the heatmaps to demonstrate the accurate key-points computation ability of our method. We have performed extensive experimentation on a publicly available dataset to show the robustness of the proposed approach and confirmed that the Efficient-ECGNet framework is reliable to classify the ECG-based COVID-19 samples.

MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy K-Means Clustering.

Melanoma is a dangerous form of skin cancer that results in the demise of patients at the developed stage. Researchers have attempted to develop automated systems for the timely recognition of this deadly disease. However, reliable and precise identification of melanoma moles is a tedious and complex activity as there exist huge differences in the mass, structure, and color of the skin lesions. Additionally, the incidence of noise, blurring, and chrominance changes in the suspected images further enhance the complexity of the detection procedure. In the proposed work, we try to overcome the limitations of the existing work by presenting a deep learning (DL) model. Descriptively, after accomplishing the preprocessing task, we have utilized an object detection approach named CornerNet model to detect melanoma lesions. Then the localized moles are passed as input to the fuzzy K-means (FLM) clustering approach to perform the segmentation task. To assess the segmentation power of the proposed approach, two standard databases named ISIC-2017 and ISIC-2018 are employed. Extensive experimentation has been conducted to demonstrate the robustness of the proposed approach through both numeric and pictorial results. The proposed approach is capable of detecting and segmenting the moles of arbitrary shapes and orientations. Furthermore, the presented work can tackle the presence of noise, blurring, and brightness variations as well. We have attained the segmentation accuracy values of 99.32% and 99.63% over the ISIC-2017 and ISIC-2018 databases correspondingly which clearly depicts the effectiveness of our model for the melanoma mole segmentation.

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease.

Machine learning techniques have lately attracted a lot of attention for their potential to execute expert-level clinical tasks, notably in the area of medical image analysis. Chest radiography is one of the most often utilized diagnostic imaging modalities in medical practice, and it necessitates timely coverage regarding the presence of probable abnormalities and disease diagnoses in the images. Computer-aided solutions for the identification of chest illness using chest radiography are being developed in medical imaging research. However, accurate localization and categorization of specific disorders in chest X-ray images is still a challenging problem due to the complex nature of radiographs, presence of different distortions, high inter-class similarities, and intra-class variations in abnormalities. In this work, we have presented an Artificial Intelligence (AI)-enabled fully automated approach using an end-to-end deep learning technique to improve the accuracy of thoracic illness diagnosis. We proposed AI-CenterNet CXR, a customized CenterNet model with an improved feature extraction network for the recognition of multi-label chest diseases. The enhanced backbone computes deep key points that improve the abnormality localization accuracy and, thus, overall disease classification performance. Moreover, the proposed architecture is lightweight and computationally efficient in comparison to the original CenterNet model. We have performed extensive experimentation to validate the effectiveness of the proposed technique using the National Institutes of Health (NIH) Chest X-ray dataset. Our method achieved an overall Area Under the Curve (AUC) of 0.888 and an average IOU of 0.801 to detect and classify the eight types of chest abnormalities. Both the qualitative and quantitative findings reveal that the suggested approach outperforms the existing methods, indicating the efficacy of our approach.

COVID-DAI: A novel framework for COVID-19 detection and infection growth estimation using computed tomography images.

The COVID-19 pandemic is spreading at a fast pace around the world and has a high mortality rate. Since there is no proper treatment of COVID-19 and its multiple variants, for example, Alpha, Beta, Gamma, and Delta, being more infectious in nature are affecting millions of people, further complicates the detection process, so, victims are at the risk of death. However, timely and accurate diagnosis of this deadly virus can not only save the patients from life loss but can also prevent them from the complex treatment procedures. Accurate segmentation and classification of COVID-19 is a tedious job due to the extensive variations in its shape and similarity with other diseases like Pneumonia. Furthermore, the existing techniques have hardly focused on the infection growth estimation over time which can assist the doctors to better analyze the condition of COVID-19-affected patients. In this work, we tried to overcome the shortcomings of existing studies by proposing a model capable of segmenting, classifying the COVID-19 from computed tomography images, and predicting its behavior over a certain period. The framework comprises four main steps: (i) data preparation, (ii) segmentation, (iii) infection growth estimation, and (iv) classification. After performing the pre-processing step, we introduced the DenseNet-77 based UNET approach. Initially, the DenseNet-77 is used at the Encoder module of the UNET model to calculate the deep keypoints which are later segmented to show the coronavirus region. Then, the infection growth estimation of COVID-19 per patient is estimated using the blob analysis. Finally, we employed the DenseNet-77 framework as an end-to-end network to classify the input images into three classes namely healthy, COVID-19-affected, and pneumonia images. We evaluated the proposed model over the COVID-19-20 and COVIDx CT-2A datasets for segmentation and classification tasks, respectively. Furthermore, unlike existing techniques, we performed a cross-dataset evaluation to show the generalization ability of our method. The quantitative and qualitative evaluation confirms that our method is robust to both COVID-19 segmentation and classification and can accurately predict the infection growth in a certain time frame. RESEARCH HIGHLIGHTS: We present an improved UNET framework with a DenseNet-77-based encoder for deep keypoints extraction to enhance the identification and segmentation performance of the coronavirus while reducing the computational complexity as well. We propose a computationally robust approach for COVID-19 infection segmentation due to fewer model parameters. Robust segmentation of COVID-19 due to accurate feature computation power of DenseNet-77. A module is introduced to predict the infection growth of COVID-19 for a patient to analyze its severity over time. We present such a framework that can effectively classify the samples into several classes, that is, COVID-19, Pneumonia, and healthy samples. Rigorous experimentation was performed including the cross-dataset evaluation to prove the efficacy of the presented technique.

An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization.

Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening-based treatment can save the patient from complete vision loss. Accurate screening procedures are dependent on the availability of human experts who performs the manual analysis of retinal samples to identify the glaucomatous-affected regions. However, due to complex glaucoma screening procedures and shortage of human resources, we often face delays which can increase the vision loss ratio around the globe. To cope with the challenges of manual systems, there is an urgent demand for designing an effective automated framework that can accurately identify the Optic Disc (OD) and Optic Cup (OC) lesions at the earliest stage. Efficient and effective identification and classification of glaucomatous regions is a complicated job due to the wide variations in the mass, shade, orientation, and shapes of lesions. Furthermore, the extensive similarity between the lesion and eye color further complicates the classification process. To overcome the aforementioned challenges, we have presented a Deep Learning (DL)-based approach namely EfficientDet-D0 with EfficientNet-B0 as the backbone. The presented framework comprises three steps for glaucoma localization and classification. Initially, the deep features from the suspected samples are computed with the EfficientNet-B0 feature extractor. Then, the Bi-directional Feature Pyramid Network (BiFPN) module of EfficientDet-D0 takes the computed features from the EfficientNet-B0 and performs the top-down and bottom-up keypoints fusion several times. In the last step, the resultant localized area containing glaucoma lesion with associated class is predicted. We have confirmed the robustness of our work by evaluating it on a challenging dataset namely an online retinal fundus image database for glaucoma analysis (ORIGA). Furthermore, we have performed cross-dataset validation on the High-Resolution Fundus (HRF), and Retinal Image database for Optic Nerve Evaluation (RIM ONE DL) datasets to show the generalization ability of our work. Both the numeric and visual evaluations confirm that EfficientDet-D0 outperforms the newest frameworks and is more proficient in glaucoma classification.

Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering.

Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effectively; however, the automated localization and segmentation of skin lesion at earlier stages is still a challenging task due to the low contrast between melanoma moles and skin portion and a higher level of color similarity between melanoma-affected and -nonaffected areas. In this paper, we present a fully automated method for segmenting the skin melanoma at its earliest stage by employing a deep-learning-based approach, namely faster region-based convolutional neural networks (RCNN) along with fuzzy k-means clustering (FKM). Several clinical images are utilized to test the presented method so that it may help the dermatologist in diagnosing this life-threatening disease at its earliest stage. The presented method first preprocesses the dataset images to remove the noise and illumination problems and enhance the visual information before applying the faster-RCNN to obtain the feature vector of fixed length. After that, FKM has been employed to segment the melanoma-affected portion of skin with variable size and boundaries. The performance of the presented method is evaluated on the three standard datasets, namely ISBI-2016, ISIC-2017, and PH2, and the results show that the presented method outperforms the state-of-the-art approaches. The presented method attains an average accuracy of 95.40, 93.1, and 95.6% on the ISIC-2016, ISIC-2017, and PH2 datasets, respectively, which is showing its robustness to skin lesion recognition and segmentation.

Analysis of Brain MRI Images Using Improved CornerNet Approach.

The brain tumor is a deadly disease that is caused by the abnormal growth of brain cells, which affects the human blood cells and nerves. Timely and precise detection of brain tumors is an important task to avoid complex and painful treatment procedures, as it can assist doctors in surgical planning. Manual brain tumor detection is a time-consuming activity and highly dependent on the availability of area experts. Therefore, it is a need of the hour to design accurate automated systems for the detection and classification of various types of brain tumors. However, the exact localization and categorization of brain tumors is a challenging job due to extensive variations in their size, position, and structure. To deal with the challenges, we have presented a novel approach, namely, DenseNet-41-based CornerNet framework. The proposed solution comprises three steps. Initially, we develop annotations to locate the exact region of interest. In the second step, a custom CornerNet with DenseNet-41 as a base network is introduced to extract the deep features from the suspected samples. In the last step, the one-stage detector CornerNet is employed to locate and classify several brain tumors. To evaluate the proposed method, we have utilized two databases, namely, the Figshare and Brain MRI datasets, and attained an average accuracy of 98.8% and 98.5%, respectively. Both qualitative and quantitative analysis show that our approach is more proficient and consistent with detecting and classifying various types of brain tumors than other latest techniques.

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model.

Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.

Impact of agile management on project performance: Evidence from I.T sector of Pakistan.

Over the past several years, global project management teams have been facing dynamic challenges that continue to grow exponentially with the increasing number of complexities associated with the undertaken tasks. The ever-evolving organizational challenges demand project managers to adapt novel management practices to accomplish organizational goals rather than following traditional management practices. Considering which, the current study aims to explain the effect of agile management practices upon project performance directly as well as while being mediated through project complexity. Furthermore, the aforementioned mediatory relationship is evaluated in terms of the moderating effect of leadership competencies. The current study utilized the survey approach to collect the data from registered I.T firms deployed in the potential metropolitans of each province of Pakistan including, Peshawar, Islamabad, Lahore, Sialkot, Faisalabad, Hyderabad, Sukkur, and Karachi. A total of 176 responses were utilized for statistical evaluations. As result, it was observed that the negative influence anticipated by project complexity on project performance was compensated by the agile management practices. Further, the leadership competencies played a pivotal role in managing project complexity while implementing agile management practices and therefore enhancing project performance. The current study abridges the potential knowledge gap conceptually by evaluating the direct impact of agile management upon project performance while considering all of its aspects, exploring the mediatory role of project performance and evaluating the moderating role of leadership competencies in attaining optimum project performance. In contextual terms, the current study fills the knowledge gap by gauging the implications of agile management practices within the I.T sector of Pakistan. The results of the current study can be a potential guide for both the academicians and the industry professionals.

A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images.

A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches.

Diabetic retinopathy detection through novel tetragonal local octa patterns and extreme learning machines.

Diabetic retinopathy (DR) is an eye disease that victimize the people suffering from diabetes from many years. The severe form of DR results in form of the blindness that can initially be controlled by the DR-screening oriented treatment. The effective screening programs require the trained human resource that manually grade the fundus images to understand the severity of the disease. But due to the complexity of this process, and the insufficient number of the trained workers, the precise manual grading is an expensive process. The CAD-based solutions try to address these limitations but most of the existing DR detection systems are as evaluated over small sets and become ineffective when applied in real scenarios. Therefore, in this paper we proposed a novel technique to precisely detect the various stages of the DR by extending the research of the content-based image retrieval domain. To achieve the human-level performance over the large-scale DR-datasets (i.e. Kaggle-DR), the fundus images are represented by the novel tetragonal local octa pattern (T-LOP) features, that are then classified through the extreme learning machine (ELM). To justify the significance of the method, the proposed scheme is compared against several state-of-the-art methods including the deep learning-based methods over four DR-datasets of variational lengths (i.e. Kaggle-DR, DRIVE, Review-DB, STARE). The experimental results confirm the significance of the DR-detection scheme to serve as a stand-alone solution for providing the precise information of the severity of the DR in an efficient manner.

Driving behaviours, traffic risk and road safety: comparative study between Malaysia and Singapore.

The present study aims to investigate differences in road safety attitude, driver behaviour and traffic risk perception between Malaysia and Singapore. A questionnaire-based survey was conducted among a sample of Singaporean (n = 187) and Malaysian (n = 313) road users. The data was analysed using confirmatory factor analysis and structural equation modelling applied to measure comparative fit indices of Malaysian and Singaporean respondents. The results show that the perceived traffic risk of Malaysian respondents is higher than Singaporean counterparts. Moreover, the structural equation modelling has confirmed perceived traffic risk performing the role of full mediation between perceived driving skills and perceived road safety for both the countries, while perceived traffic skills was found to perform the role of partial mediation between aggression and anxiety, on one hand, and road safety, on the other hand, in Malaysia and Singapore. In addition, in both countries, a weak correlation between perceived driving skills, aggression and anxiety with perceived road safety was found, while a strong correlation exists with traffic risk perception. The findings of this study have been discussed in terms of theoretical, practical and conceptual implications for both scholars and policy-makers to better understand the young drivers' attitude and behaviour relationship towards road safety measures with a view to future research.