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  • 1. Detection of lung cancer in CT scans using grey wolf optimization algorithm and recurrent neural network

    La description:
    Purpose  For radiologists, identifying and assessing thelung nodules of cancerous form from CT scans is a difficult and laborious task. As a result, early lung growing prediction is required for the investigation technique, and hence it increases the chances of a successful treatment. To ease this problem, computer-aided diagnostic (CAD) solutions have been deployed. The main purpose of the work is to detect the nodules are malignant or not and to provide the results with better accuracy. Methods  A neural network model that incorporates a feedback loop is the recurrent neural network. Evolutionary algorithms such as the Grey Wolf Optimization Algorithm and Recurrent Neural Network (RNN) Techniques are investigated utilising the Matlab Tool in this work. Statistical attributes are also produced and compared with other RNN with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)combinations for study. Results  The proposed method produced very high accuracy, sensitivity, specificity, and precision and compared with other state of art methods. Because of its simplicity and possible global search capabilities, evolutionary algorithms have shown tremendous promise in the area of feature selection in the latest years. Conclusion  The proposed techniques have demonstrated outstanding outcomes in various disciplines, outperforming classical methods. Early detection of lung nodules will aid in determining whether the nodules will become malignant or not.
    Mot-clé:
    Optimization, Lung Cancer, Recurrent Neural Network, and CT images
    Assujettir:
    Data Science and Artificial Intelligence
    Créateur:
    Roy, Sudipta, Gunjan, Vinit Kumar , Shaik, Fahimudin, and Singh, Ninni
    Owner:
    n.sakthivel@jioinstitute.edu.in
    Éditeur:
    Springer Nature
    Emplacement:
    Cham and Switzerland
    La langue:
    English
    Date de téléchargement:
    10-02-2023
    Date modifiée:
    16-02-2023
    date créée:
    01-11-2022
    Rights Statement Tesim:
    In Copyright
    License Tesim:
    All rights reserved
    Resource Type:
    Article
    Identificateur:
    10.1007/s12553-022-00700-8

  • 2. Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift

    La description:
    Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide. Apart from musculoskeletal fractures, femoral neck injuries, knee osteoarthritis, and fractures are very common bone diseases, and the rate is expected to double in the next 30 years. Therefore, proper and timely diagnosis and treatment of a fractured patient are crucial. Contrastingly, missed fractures are a common prognosis failure in accidents and emergencies. This causes complications and delays in patients’ treatment and care. These days, artificial intelligence (AI) and, more specifically, deep learning (DL) are receiving significant attention to assist radiologists in bone fracture detection. DL can be widely used in medical image analysis. Some studies in traumatology and orthopaedics have shown the use and potential of DL in diagnosing fractures and diseases from radiographs. In this systematic review, we provide an overview of the use of DL in bone imaging to help radiologists to detect various abnormalities, particularly fractures. We have also discussed the challenges and problems faced in the DL-based method, and the future of DL in bone imaging.
    Mot-clé:
    Deep Learning, Fractures, Bone Imaging, Artificial Intelligence, Radiology, and Computer Vision
    Assujettir:
    Data Science and Artificial Intelligence
    Créateur:
    Roy, Sudipta and Meena, Tanushree
    Donateur:
    Jio Institute CVMI-Computer Vision in Medical Imaging Project
    Owner:
    n.sakthivel@jioinstitute.edu.in
    Éditeur:
    MDPI
    Emplacement:
    Switzerland
    La langue:
    English
    Date de téléchargement:
    10-02-2023
    Date modifiée:
    16-02-2023
    date créée:
    01-10-2022
    Rights Statement Tesim:
    In Copyright
    License Tesim:
    All rights reserved
    Resource Type:
    Article
    Identificateur:
    10.3390/diagnostics12102420