D. Banumathy^1,*, Osamah Ibrahim Khalaf², Carlos Andrés Tavera Romero³, P. Vishnu Raja⁴, Dilip Kumar Sharma⁵

Computer Systems Science and Engineering, Vol.48, No.3, pp. 863-866, 2024, DOI:10.32604/csse.2024.053657 - 20 May 2024

Abstract This article has no abstract. More >

Imad Saud Al Naimi^1,2,*, Syed Alwee Aljunid Syed Junid¹, Muhammad lmran Ahmad^1,*, K. Suresh Manic^2,3

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2585-2608, 2024, DOI:10.32604/cmc.2024.046461 - 15 May 2024

Abstract Augmentation of abnormal cells in the brain causes brain tumor (BT), and early screening and treatment will reduce its harshness in patients. BT’s clinical level screening is usually performed with Magnetic Resonance Imaging (MRI) due to its multi-modality nature. The overall aims of the study is to introduce, test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans, facilitating improved accuracy. The research intends to devise a reliable framework for detecting the BT region in the two-dimensional (2D) MRI slice, and identifying its class with improved… More >

M. Muthalakshmi^1,*, 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 - 20 January 2023

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 More >

Muhammad Javaid Iqbal¹, Muhammad Waseem Iqbal², Muhammad Anwar^3,*, 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 - 28 December 2022

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… More >

D. Banumathy^1,*, 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 - 01 June 2022

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… More >

K. Dinesh Babu^1,*, C. Senthil Singh²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 709-720, 2023, DOI:10.32604/csse.2023.024295 - 01 June 2022

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,… More >

Federica Caldaroni¹, Massimo Chessa², Alessandro Varrica¹, Alessandro Giamberti^1,*

Congenital Heart Disease, Vol.17, No.4, pp. 491-494, 2022, DOI:10.32604/chd.2022.019119 - 04 July 2022

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 >

Prabhakaran Mathialagan^*, Malathy Chidambaranathan

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 929-946, 2022, DOI:10.32604/iasc.2022.020163 - 22 September 2021

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… More >

Simy Mary Kurian¹, Sujitha Juliet Devaraj^1,*, Vinodh P. Vijayan²

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2093-2109, 2021, DOI:10.32604/cmc.2021.018090 - 21 July 2021

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… More >

P. Veeresha¹, Esin Ilhan², D. G. Prakasha³, Haci Mehmet Baskonus⁴, Wei Gao^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1013-1036, 2021, DOI:10.32604/cmes.2021.014988 - 24 May 2021

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 More >