Browsing by Author "Tiwari, Shamik"

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    Heartbeat Abnormality Detection in Phonocardiogram Signals using Wavelet Time Scattering and Optimized KNN Classification
    (2023) Tiwari, Shamik; Maheshwari, Piyush
    Heart auscultation continues to play an essential role in heart health diagnosis. However, many places worldwide have a shortage of suitably qualified medical practitioner’s adept at this ability. This highlights the critical need to develop accurate automated systems for evaluating Phonocardiogram (PCG) data. PCGs are acoustic recordings that capture the noises made by the heart during its systolic and diastolic cycles. To solve this issue, we suggest using Wavelet Time Scattering with an optimized XGBoost classifier and K-Nearest Neighbors (KNN) classifier to detect irregular heartbeats in PCG signals. The results are promising, as the optimized KNN classifier obtains an impressive accuracy rate of 92.5% when combined with five-fold cross-validation, which is better than XGBoost classifier, which gains 87.93%. This demonstrates the efficacy of the optimized KNN in improving the automated interpretation of PCG data and assisting in the early diagnosis of heart-related problems.
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    MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models
    (IEEE, 2023) Tiwari, Shamik; Maheshwari, Piyush
    Monkeypox, a zoonotic orthopoxvirus, unintentionally produces smallpox-like sickness in people, though with a far lower death rate. Despite the fact that Deep Networks have been extensively used for visual inspection of such diesases, the majority of works have frequently relied their analysis on the results produced by a particular network without taking the response of the colour channels to classification findings into account. Deep learning has recently shown to have enormous potential for image based diagnosis, including the detection of skin cancer, the identification of tumour cells, and the COVID-19 patient diagnosis through chest radiography. As a result, a similar application may be used to identify the sickness associated with monkeypox as it impacted human skin. This image can then be obtained and employed to identify the illness. This work focused on investing the prominent color channel for Convolution Neural Network (ConvNet) based monkeypox classification using skin images. For this purpose, a transfer lerning based classification architecture named MPox-DenseConvNet with fine tuning is designed. Three colour channels namely RGB, HSV and YCbCr are analyzed using proposed MPox-DenseConvNet. The outcomes demonstrated that the colour channel employed had an impact on the performance of the classification. The results also confirmed that the HSV color channel has outperformed of all the colour channels taken into consideration.
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    PCOS-WaveConvNet: A Wavelet Convolutional Neural Network for Polycystic Ovary Syndrome Detection using Ultrasound images
    (IEEE, 2023) Tiwari, Shamik; Maheshwari, Piyush
    Women of reproductive age are susceptible to polycystic ovarian syndrome (PCOS), a hormonal condition. Multiple small follicles or cysts on the ovaries are one of the symptoms of PCOS and can be found using ultrasound imaging. Wavelet ConvNets have been applied in various applications, including image classification, object detection, and biomedical signal analysis. A Wavelet ConvNet is a type of deep learning model that applies wavelet transformation to input data before feeding it into a convolutional neural network. The wavelet transform is a mathematical technique that breaks down a signal or image into a series of sub-bands, each representing different frequency components of the original data. In this work, A 2D Discrete Wavelet Transform (2D-DWT) with the Haar wavelet is applied to each image. The resulting sub-bands namely Low-Low (LL), Low-High (LH), High-Low (HL), and High-High (HH) are concatenated to create a 4-channel feature map. Further, this concatenated feature map is fed into the ConvNet for classification. The PCOS-WaveConvNet classifier has attained 99.7% accuracy which is better than a usual ConvNet model.
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    Polycystic Ovarian Syndrome Identification through Self-Attention Guided Convolutional Neural Network
    (Institute of Electrical and Electronics Engineers Inc., 2023) Tiwari, Shamik; Maheshwari, Piyush
    Polycystic Ovarian Syndrome (PCOS) is a hormonal disorder that impacts women during their reproductive years, marked by indicators like multiple ovarian follicles or cysts that can be visualized through ultrasound imaging. Convolution Neural Networks (ConvNets) have been enhanced with self-attention mechanisms to improve their efficacy across a variety of computer vision applications, according to researchers. This study uses self-attention to improve the effectiveness of a ConvNet classifier in classifying PCOS, yielding a superior 99% accuracy, exceeding the 96% accuracy of a regular ConvNet classifier.