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  • Open Access


    Deep Learning Framework for the Prediction of Childhood Medulloblastoma

    M. Muthalakshmi1,*, T. Merlin Inbamalar2, C. Chandravathi3, K. Saravanan4

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 735-747, 2023, DOI:10.32604/csse.2023.032449

    Abstract This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma (CMB) using a well-defined deep learning architecture. A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images. First, a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes. A 10-layer deep learning architecture is designed to extract deep features. The introduction of pooling layers in the architecture reduces the feature dimension. The extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling… More >

  • Open Access


    Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

    Muhammad Javaid Iqbal1, Muhammad Waseem Iqbal2, Muhammad Anwar3,*, Muhammad Murad Khan4, Abd Jabar Nazimi5, Mohammad Nazir Ahmad6

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5267-5281, 2023, DOI:10.32604/cmc.2023.033024

    Abstract The brain tumour is the mass where some tissues become old or damaged, but they do not die or not leave their space. Mainly brain tumour masses occur due to malignant masses. These tissues must die so that new tissues are allowed to be born and take their place. Tumour segmentation is a complex and time-taking problem due to the tumour’s size, shape, and appearance variation. Manually finding such masses in the brain by analyzing Magnetic Resonance Images (MRI) is a crucial task for experts and radiologists. Radiologists could not work for large volume images simultaneously, and many errors occurred… More >

  • Open Access


    Multimodal Imaging with 3D-Holograms for Preoperative Planning in Pediatric Cardiac Surgery: A Unique Case Report

    Federica Caldaroni1, Massimo Chessa2, Alessandro Varrica1, Alessandro Giamberti1,*

    Congenital Heart Disease, Vol.17, No.4, pp. 491-494, 2022, DOI:10.32604/chd.2022.019119

    Abstract Multimodal imaging, including augmented or mixed reality, transforms the physicians’ interaction with clinical imaging, allowing more accurate data interpretation, better spatial resolution, and depth perception of the patient’s anatomy. We successfully overlay 3D holographic visualization to magnetic resonance imaging images for preoperative decision making of a complex case of cardiac tumour in a 7-year-old girl. More >

  • Open Access


    Breast Calcifications and Histopathological Analysis on Tumour Detection by CNN

    D. Banumathy1,*, Osamah Ibrahim Khalaf2, Carlos Andrés Tavera Romero3, P. Vishnu Raja4, Dilip Kumar Sharma5

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 595-612, 2023, DOI:10.32604/csse.2023.025611

    Abstract The most salient argument that needs to be addressed universally is Early Breast Cancer Detection (EBCD), which helps people live longer lives. The Computer-Aided Detection (CADs)/Computer-Aided Diagnosis (CADx) system is indeed a software automation tool developed to assist the health professions in Breast Cancer Detection and Diagnosis (BCDD) and minimise mortality by the use of medical histopathological image classification in much less time. This paper purposes of examining the accuracy of the Convolutional Neural Network (CNN), which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early identification of… More >

  • Open Access


    Brain Tumor Segmentation through Level Based Learning Model

    K. Dinesh Babu1,*, C. Senthil Singh2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 709-720, 2023, DOI:10.32604/csse.2023.024295

    Abstract Brain tumors are potentially fatal presence of cancer cells over a human brain, and they need to be segmented for accurate and reliable planning of diagnosis. Segmentation process must be carried out in different regions based on which the stages of cancer can be accurately derived. Glioma patients exhibit a different level of challenge in terms of cancer or tumors detection as the Magnetic Resonance Imaging (MRI) images possess varying sizes, shapes, positions, and modalities. The scanner used for sensing the location of tumors cells will be subjected to additional protocols and measures for accuracy, in turn, increasing the time… More >

  • Open Access


    Severity Grade Recognition for Nasal Cavity Tumours Using Décor CNN

    Prabhakaran Mathialagan*, Malathy Chidambaranathan

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 929-946, 2022, DOI:10.32604/iasc.2022.020163

    Abstract Nasal cavity and paranasal sinus tumours that occur in the respiratory tract are the most life-threatening disease in the world. The human respiratory tract has many sites which has different mucosal lining like frontal, parred, sphenoid and ethmoid sinuses. Nasal cavity tumours can occur at any different mucosal linings and chances of prognosis possibility from one nasal cavity site to another site is very high. The paranasal sinus tumours can metastases to oral cavity and digestive tracts may lead to excessive survival complications. Grading the respiratory tract tumours with dysplasia cases are more challenging using manual pathological procedures. Manual microscopic… More >

  • Open Access


    Brain Tumour Detection by Gamma DeNoised Wavelet Segmented Entropy Classifier

    Simy Mary Kurian1, Sujitha Juliet Devaraj1,*, Vinodh P. Vijayan2

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2093-2109, 2021, DOI:10.32604/cmc.2021.018090

    Abstract Magnetic resonance imaging (MRI) is an essential tool for detecting brain tumours. However, identification of brain tumours in the early stages is a very complex task since MRI images are susceptible to noise and other environmental obstructions. In order to overcome these problems, a Gamma MAP denoised Strömberg wavelet segmentation based on a maximum entropy classifier (GMDSWS-MEC) model is developed for efficient tumour detection with high accuracy and low time consumption. The GMDSWS-MEC model performs three steps, namely pre-processing, segmentation, and classification. Within the GMDSWS-MEC model, the Gamma MAP filter performs the pre-processing task and achieves a significant increase in… More >

  • Open Access


    Regarding on the Fractional Mathematical Model of Tumour Invasion and Metastasis

    P. Veeresha1, Esin Ilhan2, D. G. Prakasha3, Haci Mehmet Baskonus4, Wei Gao5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1013-1036, 2021, DOI:10.32604/cmes.2021.014988

    Abstract In this paper, we analyze the behaviour of solution for the system exemplifying model of tumour invasion and metastasis by the help of q-homotopy analysis transform method (q-HATM) with the fractional operator. The analyzed model consists of a system of three nonlinear differential equations elucidating the activation and the migratory response of the degradation of the matrix, tumour cells and production of degradative enzymes by the tumour cells. The considered method is graceful amalgamations of q-homotopy analysis technique with Laplace transform (LT), and Caputo–Fabrizio (CF) fractional operator is hired in the present study. By using the fixed point theory, existence… More >

  • Open Access


    Épidémiologie des tumeurs neuroendocrines intestinales *
    Epidemiology of Neuroendrocine Intestinal Tumours

    C. Lepage

    Oncologie, Vol.21, No.2, pp. 113-117, 2019, DOI:10.3166/onco-2019-0051

    Abstract Little is known about the epidemiology of digestive neuroendocrine tumours (NETs). NETs remain a rare cancer, representing 1% of all digestive cancers. In France, incidence rates are estimated to around 1.1/100,000 inhabitants in males and 0.9/100,000 in females. The incidence rates got increased over time, with probably more than 1,000 new cases per year in France. Because of their relatively good prognosis, NETs are the second more prevalent digestive cancer after colorectal cancer. Most digestive NETs are well-differentiated (WDNETs); poorly differentiated neuroendocrine carcinomas (PDNEC) account for less than 20% of the cases in most of the series. Among bowel-NETs, the… More >

  • Open Access


    Quand et avec quelles conséquences opérer une tumeur neuroendocrine du pancréas ou du grêle ?*
    When to Operate and What Are the Consequences of a Surgical Resection of a Neuroendrocine Tumour in the Pancreas or Small Bowel

    E. Hain, J. Gharios, R. Sindayigaya, S. Gaujoux

    Oncologie, Vol.21, No.2, pp. 91-96, 2019, DOI:10.3166/onco-2019-0048

    Abstract Neuroendocrine tumors (NET) are rare and can occur in all parts of the digestive tract. They can be functional or non-functional. All patients presenting NET should be discussed for the surgical management within the RENATEN tumor board. For sporadic pancreatic NET, surgery is recommended for non-functional lesion >2 cm in size and/or associated with ductal dilatation. For non-aggressive tumor, parenchyma-sparing surgery should be preferred to avoid exocrine and endocrine pancreatic insufficiency. For small bowel NET, surgery must always be considered to avoid complications such as small bowel obstruction. Lymphadenectomy must include at least 8 noded. Surgery must avoid short bowel… More >

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