Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being ...with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning‐based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation‐based selection method and as the output, the best features are selected. These selected features are validated through feed‐forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.
3D convolutional neural network (CNN) architecture is proposed for tumor extraction. The pretrained VGG19 CNN model is utilized for feature extraction. Correlation‐based along FNN‐based best features are selected. Results are validated for segmentation and classification steps.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Acute Leukemia is a life‐threatening disease common both in children and adults that can lead to death if left untreated. Acute Lymphoblastic Leukemia (ALL) spreads out in children's bodies rapidly ...and takes the life within a few weeks. To diagnose ALL, the hematologists perform blood and bone marrow examination. Manual blood testing techniques that have been used since long time are often slow and come out with the less accurate diagnosis. This work improves the diagnosis of ALL with a computer‐aided system, which yields accurate result by using image processing and deep learning techniques. This research proposed a method for the classification of ALL into its subtypes and reactive bone marrow (normal) in stained bone marrow images. A robust segmentation and deep learning techniques with the convolutional neural network are used to train the model on the bone marrow images to achieve accurate classification results. Experimental results thus obtained and compared with the results of other classifiers Naïve Bayesian, KNN, and SVM. Experimental results reveal that the proposed method achieved 97.78% accuracy. The obtained results exhibit that the proposed approach could be used as a tool to diagnose Acute Lymphoblastic Leukemia and its sub‐types that will definitely assist pathologists.
This research proposed a method for the classification of Acute Lymphoblastic Leukemia (ALL) into its subtypes and reactive bone marrow (Normal) in stained bone marrow images using deep learning techniques with convolutional neural networks.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Heart diseases are currently a major cause of death in the world. This problem is severe in developing countries in Africa and Asia. A heart disease predicted at earlier stages not only helps the ...patients prevent it, but I can also help the medical practitioners learn the major causes of a heart attack and avoid it before its actual occurrence in patient. In this paper, we propose a method named CardioHelp which predicts the probability of the presence of cardiovascular disease in a patient by incorporating a deep learning algorithm called convolutional neural networks (CNN). The proposed method is concerned with temporal data modeling by utilizing CNN for HF prediction at its earliest stage. We prepared the heart disease dataset and compared the results with state-of-the-art methods and achieved good results. Experimental results show that the proposed method outperforms the existing methods in terms of performance evaluation metrics. The achieved accuracy of the proposed method is 97%.
•A technique for copy-move forgery detection in images is proposed via SWT and DCT.•The technique utilizes SWT based features for exposing forgeries in digital images.•The dimension of feature ...vectors is reduced by applying block DCT to each block.•The results show that the technique outperforms in terms of TDR and FDR.
In this era, due to the widespread availability of digital devices, various open source and commercially available image editing tools have made authenticity of image contents questionable. Copy-move forgery (CMF) is a common technique to produce tampered images by concealing undesirable objects or replicating desirable objects in the same image. Therefore, means are required to authenticate image contents and identify the tampered areas. In this paper, a robust technique for CMF detection and localization in digital images is proposed. The technique extracts stationary wavelet transform (SWT) based features for exposing the forgeries in digital images. SWT is adopted because of its impressive localization properties, in both spectral and spatial domains. More specifically approximation subband of the stationary wavelet transform is utilized as this subband holds most of the information that is best suited for forgery detection. The dimension of the feature vectors is reduced by applying discrete cosine transform (DCT). To evaluate the proposed technique, we use two standard datasets namely, the CoMoFoD and the UCID for experimentations. The experimental results reveal that the proposed technique outperforms the existing techniques in terms of true and false detection rate. Consequently, the proposed forgery detection technique can be applied to detect the tampered areas and the benefits can be obtained in image forensic applications.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Alzheimer’s disease (AD) is an incurable neurodegenerative disorder accounting for 70%–80% dementia cases worldwide. Although, research on AD has increased in recent years, however, the complexity ...associated with brain structure and functions makes the early diagnosis of this disease a challenging task. Resting-state functional magnetic resonance imaging (rs-fMRI) is a neuroimaging technology that has been widely used to study the pathogenesis of neurodegenerative diseases. In literature, the computer-aided diagnosis of AD is limited to binary classification or diagnosis of AD and MCI stages. However, its applicability to diagnose multiple progressive stages of AD is relatively under-studied. This study explores the effectiveness of rs-fMRI for multi-class classification of AD and its associated stages including CN, SMC, EMCI, MCI, LMCI, and AD. A longitudinal cohort of resting-state fMRI of 138 subjects (25 CN, 25 SMC, 25 EMCI, 25 LMCI, 13 MCI, and 25 AD) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) is studied. To provide a better insight into deep learning approaches and their applications to AD classification, we investigate ResNet-18 architecture in detail. We consider the training of the network from scratch by using single-channel input as well as performed transfer learning with and without fine-tuning using an extended network architecture. We experimented with residual neural networks to perform AD classification task and compared it with former research in this domain. The performance of the models is evaluated using precision, recall, f1-measure, AUC and ROC curves. We found that our networks were able to significantly classify the subjects. We achieved improved results with our fine-tuned model for all the AD stages with an accuracy of 100%, 96.85%, 97.38%, 97.43%, 97.40% and 98.01% for CN, SMC, EMCI, LMCI, MCI, and AD respectively. However, in terms of overall performance, we achieved state-of-the-art results with an average accuracy of 97.92% and 97.88% for off-the-shelf and fine-tuned models respectively. The Analysis of results indicate that classification and prediction of neurodegenerative brain disorders such as AD using functional magnetic resonance imaging and advanced deep learning methods is promising for clinical decision making and have the potential to assist in early diagnosis of AD and its associated stages.
For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this ...article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Trace heavy metals, such as arsenic, cadmium, lead, chromium, nickel, and mercury, are important environmental pollutants, particularly in areas with high anthropogenic pressure. In addition to these ...metals, copper, manganese, iron, and zinc are also important trace micronutrients. The presence of trace heavy metals in the atmosphere, soil, and water can cause serious problems to all organisms, and the ubiquitous bioavailability of these heavy metal can result in bioaccumulation in the food chain which especially can be highly dangerous to human health. This study reviews the heavy metal contamination in several areas of Pakistan over the past few years, particularly to assess the heavy metal contamination in water (ground water, surface water, and waste water), soil, sediments, particulate matter, and vegetables. The listed contaminations affect the drinking water quality, ecological environment, and food chain. Moreover, the toxicity induced by contaminated water, soil, and vegetables poses serious threat to human health.
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DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
The purpose of the study was to assess the knowledge, attitude, and practices of dentists of twin cities regarding the use of endodontic posts in root canal treated tooth. A questionnaire was created ...and distributed among dentists of Rawalpindi and Islamabad via social media platforms regarding the use of posts. The results revealed that majority (60%) of the participants used endodontic posts for teeth with adequate ferrule, and believed that the function of endodontic posts is to retain the core material (50.5%). Glass fibre posts were preferred for anterior teeth (87%), whereas metal posts were favoured in posterior teeth (63%). It was concluded that the main function of the endodontic post is to retain the core material. The commonest indication is when there is at least 2mm of ferrule present and the optimal post length is 2/3rd of the root canal.
The recent era has witnessed exponential growth in the production of multimedia data which initiates exploration and expansion of certain domains that will have an overwhelming impact on human ...society in near future. One of the domains explored in this article is content-based image retrieval (CBIR), in which images are mostly encoded using hand-crafted approaches that employ different descriptors and their fusions. Although utilization of these approaches has yielded outstanding results, their performance in terms of a semantic gap, computational cost, and appropriate fusion based on problem domain is still debatable. In this article, a novel CBIR method is proposed which is based on the transfer learning-based visual geometry group (VGG-19) method, genetic algorithm (GA), and extreme learning machine (ELM) classifier. In the proposed method, instead of using hand-crafted features extraction approaches, features are extracted automatically using a transfer learning-based VGG-19 model to consider both local and global information of an image for robust image retrieval. As deep features are of high dimension, the proposed method reduces the computational expense by passing the extracted features through GA which returns a reduced set of optimal features. For image classification, an extreme learning machine classifier is incorporated which is much simpler in terms of parameter tuning and learning time as compared to other traditional classifiers. The performance of the proposed method is evaluated on five datasets which highlight the better performance in terms of evaluation metrics as compared with the state-of-the-art image retrieval methods. Its statistical analysis through a nonparametric Wilcoxon matched-pairs signed-rank test also exhibits significant performance.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Assistive technology (AT) involvement in therapeutic treatment has provided simple and efficient healthcare solutions to people. Within a short span of time, mobile health (mHealth) has grown rapidly ...for assisting people living with a chronic disorder. This research paper presents the comprehensive study to identify and review existing mHealth dementia applications (apps), and also synthesize the evidence of using these applications in assisting people with dementia including Alzheimer’s disease (AD) and their caregivers. Six electronic databases searched with the purpose of finding literature-based evidence. The search yielded 2818 research articles, with 29 meeting quantified inclusion and exclusion criteria. Six groups and their associated sub-groups emerged from the literature. The main groups are (1) activities of daily living (ADL) based cognitive training, (2) monitoring, (3) dementia screening, (4) reminiscence and socialization, (5) tracking, and (6) caregiver support. Moreover, two commercial mobile application stores i.e., Apple App Store (iOS) and Google Play Store (Android) explored with the intention of identifying the advantages and disadvantages of existing commercially available dementia and AD healthcare apps. From 678 apps, a total of 38 mobile apps qualified as per defined exclusion and inclusion criteria. The shortlisted commercial apps generally targeted different aspects of dementia as identified in research articles. This comprehensive study determined the feasibility of using mobile Health based applications for dementia including AD individuals and their caregivers regardless of limited available research, and these apps have capability to incorporate a variety of strategies and resources to dementia community care.
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