Yazar "Kumar, Basant" seçeneğine göre listele

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    Automatic arrhythmia detection based on the probabilistic neural network with FPGA implementation
    (Hindawi Ltd, 2022) Srivastava, Rohini; Kumar, Basant; Alenezi, Fayadh; Alhudhaif, Adi; Althubiti, Sara A.; Polat, Kemal
    This paper presents a prototype implementation of arrhythmia classification using Probabilistic neural network (PNN). Arrhythmia is an irregular heartbeat, resulting in severe heart problems if not diagnosed early. Therefore, accurate and robust arrhythmia classification is a vital task for cardiac patients. The classification of ECG has been performed using PNN into eight ECG classes using a unique combination of six ECG features: heart rate, spectral entropy, and 4th order of autoregressive coefficients. In addition, FPGA implementation has been proposed to prototype the complete system of arrhythmia classification. Artix-7 board has been used for the FPGA implementation for easy and fast execution of the proposed arrhythmia classification. As a result, the average accuracy for ECG classification is found to be 98.27%, and the time consumed in the classification is found to be 17 seconds.
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    Automatic detection of hard exudates shadow region within retinal layers of OCT images
    (Hindawi Ltd, 2022) Singh, Maninder; Gupta, Vishal; Singh, Pramod Kumar; Gupta, Rajeev; Kumar, Basant; Polat, Kemal
    The optical coherence tomography (OCT) is useful in viewing cross-sectional retinal images and detecting various forms of retinal disorders from those images. Image processing methods and computational algorithms underlying this paper try to detect the shadowing region beneath exudates automatically. This paper presents a novel method for detecting hard exudates from retinal OCT images, often associated with macular edema near or within the outer plexiform layer. In this paper, an algorithm can automatically detect the presence of hard exudates in retinal OCT images, and these exudates appear as highly reflective spots. Still, they do not appear as noticeable bright spots because of their minute sizes in predevelopment phases. In the proposed work, we are using a method to detect the presence of hard exudates by analyzing their shadowing effect instead of focusing on brightness spots. The raster scanning operation is performed by traversing the retina horizontally, and noting up any change in normalized summation of brightness intensity (summing up the intensity from top to bottom retinal layers and normalized concerning retinal width) leads to the detection of minute as well as the presence for the detection of large exudates detection by differentiating this brightness intensity graph. The shadow region helps identify the hard exudates; in our proposed method, the output for three input images has been shown. There is an excellent agreement between the results generated by the proposed algorithm and the diagnostic opinion made by the ophthalmologist. The proposed method automatically detects the hard exudates using shadow regions, and it does not need any parameter settings or manual intervention. It can yield significant results by giving the position of shadow regions, which indicates the presence of exudates.