Osama S. Faragallah^1,*, Ghada Abdel-Aziz², Walid El-Shafai³, Hala S. El-sayed⁴, S.F. El-Zoghdy⁵, Gamal G.N. Geweid^6,7

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 15-29, 2021, DOI:10.32604/iasc.2021.017616

Abstract The process of segmentation of the cardiac image aims to limit the inner and outer walls of the heart to segment all or portions of the organ’s boundaries. Due to its accurate morphological information, magnetic resonance (MR) images are typically used in cardiac segmentation as they provide the best contrast of soft tissues. The data acquired from the resulting cardiac images simplifies not only the laboratory assessment but also other conventional diagnostic techniques that provide several useful measures to evaluate and diagnose cardiovascular disease (CVD). Therefore, scientists have offered numerous segmentation schemes to remedy these issues for producing more accurate… More >

Hanan Abdullah Mengash^1,*, Hanan A. Hosni Mahmoud^2,3

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1551-1563, 2021, DOI:10.32604/cmc.2021.016907

Abstract Detection of brain tumors in MRI images is the first step in brain cancer diagnosis. The accuracy of the diagnosis depends highly on the expertise of radiologists. Therefore, automated diagnosis of brain cancer from MRI is receiving a large amount of attention. Also, MRI tumor detection is usually followed by a biopsy (an invasive procedure), which is a medical procedure for brain tumor classification. It is of high importance to devise automated methods to aid radiologists in brain cancer tumor diagnosis without resorting to invasive procedures. Convolutional neural network (CNN) is deemed to be one of the best machine learning… More >

Hamid A. Jalab^1,*, Rabha W. Ibrahim², Ali M. Hasan³, Faten Khalid Karim⁴, Ala’a R. Al-Shamasneh¹, Dumitru Baleanu^5,6,7

CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1467-1483, 2021, DOI:10.32604/cmc.2021.016047

Abstract The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images. The captured images may present with low contrast and low visibility, which might influence the accuracy of the diagnosis process. To overcome this problem, this paper presents a new fractional integral entropy (FITE) that estimates the unforeseeable probabilities of image pixels, posing as the main contribution of the paper. The proposed model dynamically enhances the image based on the image contents. The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’… More >

G. Oldrini, P. Henrot, F. Marchal

Oncologie, Vol.21, No.1, pp. 17-21, 2019, DOI:10.3166/onco-2019-0033

Abstract Breast cancer is the first female cancer in France and its early detection is essential. Breast MRI is an element of choice in its diagnosis but it has high direct and indirect costs because of its duration which slows down its wider use. Given its elements, the use of an abbreviated protocol develops to overcome these disadvantages. Early literature data suggests that this faster examination also allows for a shorter interpretation time. In addition, the sensitivity and specificity of the examination are not inferior to that of the complete protocol. This article explains the new concept and its interest, compares… More >

S. Dejust

Oncologie, Vol.21, No.1, pp. 5-10, 2019, DOI:10.3166/onco-2019-0031

Abstract Axillary evaluation is a major step in the initial staging of breast cancer. Ultrasound guided biopsy is currently recommended in first-line. MRI and 18FDG PET/CT are useful in axillary lymph node evaluation. Imaging sensitivities and specificities are globally identical and their combination allows obtaining the best performances. Currently, sentinel node technique is essential in case of T1-T2 N0 mammary tumors and in case of suspected lymph node adenopathy with negative cytopuncture or microbiopsy.


Résumé
L’exploration préthérapeutique axillaire est une étape majeure du bilan initial du cancer du sein. L’échographie associée à un prélèvement est actuellement recommandée en première intention. L’IRM et la… More >

Oday Ali Hassen¹, Sarmad Omar Abter², Ansam A. Abdulhussein³, Saad M. Darwish^4,*, Yasmine M. Ibrahim⁴, Walaa Sheta⁵

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 961-981, 2021, DOI:10.32604/cmc.2021.014404

Abstract Medical image segmentation has consistently been a significant topic of research and a prominent goal, particularly in computer vision. Brain tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy preparation. To prevent or minimize manual segmentation error, automated tumor segmentation, and detection became the most demanding process for radiologists and physicians as the tumor often has complex structures. Many methods for detection and segmentation presently exist, but all lack high accuracy. This paper’s key contribution focuses on evaluating machine learning… More >

Gurparsad Singh Suri¹, Gurleen Kaur¹, Sara Moein^2,*

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 723-735, 2021, DOI:10.32604/iasc.2021.015049

Abstract Schizophrenia (SZ) is a mental heterogeneous psychiatric disorder with unknown cause. Neuroscientists postulate that it is related to brain networks. Recently, scientists applied machine learning (ML) and artificial intelligence for the detection, monitoring, and prognosis of a range of diseases, including SZ, because these techniques show a high performance in discovering an association between disease symptoms and disease. Regions of the brain have significant connections to the symptoms of SZ. ML has the power to detect these associations. ML interests researchers because of its ability to reduce the number of input features when the data are high dimensional. In this… More >

Amr M. S. Mahdy^1,2,*, Mahmoud Higazy^1,3, Mohamed S. Mohamed^1,4

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3463-3486, 2021, DOI:10.32604/cmc.2021.015161

Abstract In this article, the reduced differential transform method is introduced to solve the nonlinear fractional model of Tumor-Immune. The fractional derivatives are described in the Caputo sense. The solutions derived using this method are easy and very accurate. The model is given by its signal flow diagram. Moreover, a simulation of the system by the Simulink of MATLAB is given. The disease-free equilibrium and stability of the equilibrium point are calculated. Formulation of a fractional optimal control for the cancer model is calculated. In addition, to control the system, we propose a novel modification of its model. This modification is… More >

Ishu Anand¹, Himani Negi¹, Deepika Kumar¹, Mamta Mittal², Tai-hoon Kim^3,*, Sudipta Roy⁴

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3107-3127, 2021, DOI:10.32604/cmc.2021.014229

Abstract Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. Two features substantially influence the classification accuracy of malignancy and benignity in automated cancer diagnostics. These are the precision of tumor segmentation and appropriateness of extracted attributes required for the diagnosis. In this research, the authors have proposed a ResU-Net (Residual U-Network) model for breast tumor segmentation. The proposed methodology renders augmented, and precise identification of tumor regions and produces accurate breast tumor segmentation in contrast-enhanced MR images.… More >

Neelum Noreen^1,*, Sellapan Palaniappan¹, Abdul Qayyum², Iftikhar Ahmad³, Madini O. Alassafi³

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3967-3982, 2021, DOI:10.32604/cmc.2021.014158

Abstract Brain tumors are life-threatening for adults and children. However, accurate and timely detection can save lives. This study focuses on three different types of brain tumors: Glioma, meningioma, and pituitary tumors. Many studies describe the analysis and classification of brain tumors, but few have looked at the problem of feature engineering. Methods are needed to overcome the drawbacks of manual diagnosis and conventional feature-engineering techniques. An automatic diagnostic system is thus necessary to extract features and classify brain tumors accurately. While progress continues to be made, the automatic diagnoses of brain tumors still face challenges of low accuracy and high… More >