Browsing by Author "Kaur, Palwinder"

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    Classification of Breast Cancer Mammographic Images Using A Light-Weighted Convolutional Neural Network
    (Institute of Electrical and Electronics Engineers Inc., 2023-04-10T00:00:00) Kaur, Palwinder; Kaur, Amandeep
    Deep learning is a method demanded by radiologists to assist them interpret and classify medical images correctly. A Convolutional Neural Network (CNN) is the most widely used method for classifying and analysing images. In this paper, a light-weighted CNN is presented for breast cancer classification using a dataset of breast mammography images. The suggested methodology improves the classification of mammary cancer images to assist radiologists in the detection of mammary cancer. The application of the proposed model can help in the diagnosis of mammary cancer using digital mammograms without any preceding information about the existence of a cancerous lesion. The proposed CNN can categorize the input medical images as malignant or benign with an accuracy of 99.35% which is the highest accuracy achieved for such a large mammography dataset. � 2023 IEEE.
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    Review of Progress in Diagnostic Studies of Autism Spectrum Disorder Using Neuroimaging
    (Springer Science and Business Media Deutschland GmbH, 2023-01-12T00:00:00) Kaur, Palwinder; Kaur, Amandeep
    This review article summarizes the recent advances in the diagnostic studies of autism spectrum disorders (ASDs) considering some of the most influential research articles from the last two decades. ASD is a heterogeneous neurodevelopmental disorder characterized by abnormalities in social interaction, communication, and behavioral patterns as well as some unique strengths and differences. The current diagnosis systems are based on autism diagnostic observation schedule (ADOS) or autism diagnostic interview-revised (ADI-R), but biological markers are also important for an effective diagnosis of ASDs. The amalgamation of neuroimaging techniques, such as structural and functional magnetic resonance imaging (sMRI and fMRI), with machine-learning and deep-learning approaches helps throw new light on typical biological markers of ASDs at the early stage of life. To assess the performance of a deep neural network, we develop a light-weighted CNN model for ASD classification. The overall accuracy, precision, and F1-score of the proposed model are 99.92%, 99.93% and 99.92%, respectively. All the neuroimaging studies we have reviewed can be divided into 3 categories, viz. thickness, volume and functional connectivity-based studies. We conclude with a discussion of the major findings of considered studies and promising directions for future research in this field. Graphical Abstract: [Figure not available: see fulltext.] � 2023, International Association of Scientists in the Interdisciplinary Areas.