Sameh Abd ElGhany^1,2, Mai Ramadan Ibraheem³, Madallah Alruwaili⁴, Mohammed Elmogy^5,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 117-135, 2021, DOI:10.32604/cmc.2021.016102

Abstract With the massive success of deep networks, there have been significant efforts to analyze cancer diseases, especially skin cancer. For this purpose, this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion images. This paper aims to develop and fine-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic images. Fine-tuning is a powerful method to obtain enhanced classification results by the customized pre-trained network. Regularization, batch normalization, and hyperparameter optimization are performed for fine-tuning the proposed deep network. The proposed fine-tuned ResNet50 model successfully classified 7-respective classes of dermoscopic lesions… 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 >

Jacqueline S. Lee^1,2, Angelica Blais^1,2, Julia Jackson¹, Bhavika J. Patel¹, Lillian Lai⁴, Gary Goldfield^1,3, Renee Sananes⁵, Patricia E. Longmuir^1,2,3,*

Congenital Heart Disease, Vol.16, No.3, pp. 255-267, 2021, DOI:10.32604/CHD.2021.014628

Abstract Background: Pediatric cardiology patients often experience decreased quality of life (QoL) and higher rates of mental illness, particularly with severe disease, but the relationship between them and comparisons across diagnostic groups are limited. This mixed-methods cross-sectional study assessed the association between QoL anxiety and behavior problems among children with structural heart disease, arrhythmia, or other cardiac diagnoses. Methods: Children (6–14 years, n = 76, 50% female) and their parents completed measures of QoL (PedsQL), behavior (BASC-2, subset of 19 children) and anxiety (MASC-2, children 8+ years). Pearson correlations/regression models examined associations between QoL, behavior and anxiety, controlling for age, sex,… More >

Ali M. Aseere^1,*, Ayodele Lasisi²

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 683-699, 2021, DOI:10.32604/iasc.2021.014811

Abstract In the absence of fast and adequate measures to combat them, life-threatening diseases are catastrophic to human health. Computational intelligent algorithms characterized by their adaptability, robustness, diversity, and recognition abilities allow for the diagnosis of medical diseases. This enhances the decision-making process of physicians. The objective is to predict and classify diseases accurately. In this paper, we proposed a multi-agent stacked ensemble classifier based on a vaguely quantified rough set, simple logistic algorithm, sequential minimal optimization (SMO), and JRip. The vaguely quantified rough set (VQRS) is used for feature selection and eradicating noise in the data. There are two classifier… More >

Charles Arputham^1,*, Krishnaraj Nagappan², Lenin Babu Russeliah³, AdalineSuji Russeliah⁴

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 747-759, 2021, DOI:10.32604/iasc.2021.012077

Abstract Breast cancer detection is a crucial topic in the healthcare sector. Breast cancer is a major reason for the increased mortality rate in recent years among women, specifically in developed and underdeveloped countries around the world. The incidence rate is less in India than in developed countries, but awareness must be increased. This paper focuses on an efficient deep learning-based diagnosis and classification technique to detect breast cancer from mammograms. The model includes preprocessing, segmentation, feature extraction, and classification. At the initial level, Laplacian filtering is applied to identify the portions of edges in mammogram images that are highly sensitive… More >

V. Vidya Lakshmi^1,*, J. S. Leena Jasmine²

Computer Systems Science and Engineering, Vol.37, No.2, pp. 233-245, 2021, DOI:10.32604/csse.2021.015700

Abstract Melanoma or skin cancer is the most dangerous and deadliest disease. As the incidence and mortality rate of skin cancer increases worldwide, an automated skin cancer detection/classification system is required for early detection and prevention of skin cancer. In this study, a Hybrid Artificial Intelligence Model (HAIM) is designed for skin cancer classification. It uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron (EWHMLP) for the classification. Though the wavelet transform is a powerful tool for signal and image processing, it is unable to detect the intermediate dimensional structures of a medical… More >

Kaustubh Arun Bhavsar¹, Ahed Abugabah², Jimmy Singla^1,*, Ahmad Ali AlZubi³, Ali Kashif Bashir⁴, Nikita⁵

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1997-2014, 2021, DOI:10.32604/cmc.2021.014943

Abstract The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine… More >

Sally M. Elghamrawy¹, Aboul Ella Hassnien^2,*, Vaclav Snasel³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2353-2371, 2021, DOI:10.32604/cmc.2021.014767

Abstract Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients. This study aimed to develop a COVID-19 diagnosis and prediction (AIMDP) model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography (CT) scans. The proposed system uses convolutional neural networks (CNNs) as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment. We employed the whale optimization… More >

Siddhant Banyal¹, Rinky Dwivedi², Koyel Datta Gupta², Deepak Kumar Sharma^3,*, Fadi Al-Turjman⁴, Leonardo Mostarda⁵

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1679-1696, 2021, DOI:10.32604/cmc.2021.014387

Abstract The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine… More >

Kaustubh Arun Bhavsar¹, Jimmy Singla¹, Yasser D. Al-Otaibi², Oh-Young Song^3,*, Yousaf Bin Zikria⁴, Ali Kashif Bashir⁵

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 107-125, 2021, DOI:10.32604/cmc.2021.014604

Abstract Decision making in case of medical diagnosis is a complicated process. A large number of overlapping structures and cases, and distractions, tiredness, and limitations with the human visual system can lead to inappropriate diagnosis. Machine learning (ML) methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis. Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published. Hence, to determine the use of ML to improve the diagnosis in varied medical disciplines, a systematic review is conducted in this study. To carry… More >