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Abdul Based

Bio: Abdul Based is an academic researcher from Dhaka International University. The author has contributed to research in topics: Overfitting & Retinopathy. The author has an hindex of 1, co-authored 2 publications receiving 16 citations.

Papers
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Journal ArticleDOI
20 Feb 2021-Sensors
TL;DR: In this paper, a feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%.
Abstract: Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly around the world, thought to be originated from Wuhan in China and is responsible for a large number of deaths. Earlier detection of the COVID-19 through accurate diagnosis, particularly for the cases with no obvious symptoms, may decrease the patient’s death rate. Chest X-ray images are primarily used for the diagnosis of this disease. This research has proposed a machine vision approach to detect COVID-19 from the chest X-ray images. The features extracted by the histogram-oriented gradient (HOG) and convolutional neural network (CNN) from X-ray images were fused to develop the classification model through training by CNN (VGGNet). Modified anisotropic diffusion filtering (MADF) technique was employed for better edge preservation and reduced noise from the images. A watershed segmentation algorithm was used in order to mark the significant fracture region in the input X-ray images. The testing stage considered generalized data for performance evaluation of the model. Cross-validation analysis revealed that a 5-fold strategy could successfully impair the overfitting problem. This proposed feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%. When compared to other classification techniques, such as ANN, KNN, and SVM, the CNN technique used in this study showed better classification performance. K-fold cross-validation demonstrated that the proposed feature fusion technique (98.36%) provided higher accuracy than the individual feature extraction methods, such as HOG (87.34%) or CNN (93.64%).

73 citations

Proceedings ArticleDOI
02 Apr 2021
TL;DR: In this paper, an ensemble of 5 models using the optimal transfer learning model, EfficientNet-B5 with 2 phase training was proposed to early screen diabetic retinopathy and achieved a high metric (quadratic weighted kappa score of 0.961).
Abstract: Diabetic Retinopathy is a diabetes complication that causes permanent blindness. This also affects both eyes, beginning with no visual symptoms. Yet it may lead to permanent blindness without adequate treatment. Early detection of diabetic retinopathy can be an opportunity to prevent vision loss. It involves many complicated and costly treatment methodologies and sophisticated analysis of retinal fundus images by expert doctors. Many researchers proposed different image processing and segmentation techniques. The unavailability of good quality of fundus images made the techniques unstable. Deep learning showed significant performance in various medical fields, including diabetic retinopathy. But due to a lack of high cost labeled sample dataset and performance issues due to the use of large parameters, made the approaches inefficient. In this paper, we have proposed an ensemble of 5 models using the optimal transfer learning model, EfficientNet-B5 with 2 phase training. We trained the model with a large number of sample datasets from Kaggle. Our model can early screen diabetic retinopathy and also achieved a high metric (quadratic weighted kappa score of 0.961).

5 citations


Cited by
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Journal ArticleDOI
TL;DR: CovidXrayNet as discussed by the authors is a state-of-the-art CXR-based network for detecting COVID-19 from CXRs in terms of validation accuracy.

60 citations

Journal ArticleDOI
02 Nov 2021-Sensors
TL;DR: In this article, a new automated technique is proposed using parallel fusion and optimization of deep learning models for case classification of COVID-19 case classification, which starts with a contrast enhancement using a combination of top-hat and Wiener filters, and features are extracted and fused using a parallel fusion approach-parallel positive correlation.
Abstract: In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach-parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.

52 citations

Journal ArticleDOI
TL;DR: The authors found that demographic, socioeconomic, and behavioral variables such as gender, age, subjective health status, children, household income, household assets, financial literacy, future anxiety, and myopic view of the future are associated with willingness to take a COVID-19 vaccine.
Abstract: The worldwide COVID-19 vaccination program is already underway, raising hopes and aspirations to contain the spread of the COVID-19 pandemic that halted economic and social activities. However, the issue of vaccine effectiveness and its side-effects is influencing the potential acceptance of vaccines. In this uncertain situation, we used data from a nationwide survey in Japan during February 2021, following the Japanese government's initial phase of COVID-19 vaccination. Our results show that 47% of the respondents are willing to take a vaccine once it is available, while 22% are not willing and another 31% remain indecisive. Our ordered probit regression results show that demographic, socioeconomic, and behavioral variables such as gender, age, subjective health status, children, household income, household assets, financial literacy, future anxiety, and myopic view of the future are associated with willingness to take a COVID-19 vaccine. Our findings suggest that Japan's government should not adopt a one-size-fits-all policy to promote the vaccination program, but rather target people with specific socioeconomic backgrounds who are less willing and more hesitant to take a vaccine.

44 citations

Journal ArticleDOI
TL;DR: In this article , the authors proposed a novel detection and classification approach (DCCNet) for quick diagnosis of COVID-19 using chest X-ray images of patients using CNN and histogram of oriented gradients (HOG).

36 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a deep learning based framework to enhance the diagnostic values of chest X-ray images for improved clinical outcomes, which is realized as a variant of the conventional SqueezeNet classifier with segmentation capabilities.

27 citations