Jaber S. Alzahrani¹, Reem M. Alshehri², Mohammad Alamgeer³, Anwer Mustafa Hilal^4,*, Abdelwahed Motwakel⁴, Ishfaq Yaseen⁴

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4267-4281, 2022, DOI:10.32604/cmc.2022.027369

Abstract Recently, medical data classification becomes a hot research topic among healthcare professionals and research communities, which assist in the disease diagnosis and decision making process. The latest developments of artificial intelligence (AI) approaches paves a way for the design of effective medical data classification models. At the same time, the existence of numerous features in the medical dataset poses a curse of dimensionality problem. For resolving the issues, this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data (FSS-AICBD) technique. The FSS-AICBD technique intends to derive a useful set of features and thereby… More >

Mahmoud Ragab^1,2,3,*, Ashwag Albukhari^2,4

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5577-5591, 2022, DOI:10.32604/cmc.2022.026715

Abstract Colorectal cancer is one of the most commonly diagnosed cancers and it develops in the colon region of large intestine. The histopathologist generally investigates the colon biopsy at the time of colonoscopy or surgery. Early detection of colorectal cancer is helpful to maintain the concept of accumulating cancer cells. In medical practices, histopathological investigation of tissue specimens generally takes place in a conventional way, whereas automated tools that use Artificial Intelligence (AI) techniques can produce effective results in disease detection performance. In this background, the current study presents an Automated AI-empowered Colorectal Cancer Detection and Classification (AAI-CCDC) technique. The proposed… More >

Ahmad Alassaf, Mohamed Yacin Sikkandar^*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5273-5285, 2022, DOI:10.32604/cmc.2022.025577

Abstract Malaria is a severe disease caused by Plasmodium parasites, which can be detected through blood smear images. The early identification of the disease can effectively reduce the severity rate. Deep learning (DL) models can be widely employed to analyze biomedical images, thereby minimizing the misclassification rate. With this objective, this study developed an intelligent deep-transfer-learning-based malaria parasite detection and classification (IDTL-MPDC) model on blood smear images. The proposed IDTL-MPDC technique aims to effectively determine the presence of malarial parasites in blood smear images. In addition, the IDTL-MPDC technique derives median filtering (MF) as a pre-processing step. In addition, a residual… More >

Mahmoud Ragab^1,2,3,*, Diaa Hamed^4,5

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 4185-4200, 2022, DOI:10.32604/cmc.2022.027074

Abstract Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making. Besides, the advances of machine learning (ML) techniques assist to perform the effective classification task. With this motivation, this paper presents a Fuzzy Clustering Approach Based on Breadth-first Search Algorithm (FCA-BFS) with optimal support vector machine (OSVM) model, named FCABFS-OSVM for medical data classification. The proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification models. Besides, the proposed FCABFS-OSVM technique involves the design of FCABFS technique to cluster the medical data… More >

Areej A. Malibari¹, Siwar Ben Haj Hassine², Abdelwahed Motwakel³, Manar Ahmed Hamza^3,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2859-2875, 2022, DOI:10.32604/cmc.2022.026338

Abstract Atherosclerosis diagnosis is an inarticulate and complicated cognitive process. Researches on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical decisions. Since the medical diagnostic outcomes need to be prompt and accurate, the recently developed artificial intelligence (AI) and deep learning (DL) models have received considerable attention among research communities. This study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification (MDL-BADDC) model. The proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing, feature selection, classification, and parameter tuning. Besides, the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer… More >

Areej A. Malibari¹, Reem Alshahrani², Fahd N. Al-Wesabi^3,*, Siwar Ben Haj Hassine³, Mimouna Abdullah Alkhonaini⁴, Anwer Mustafa Hilal⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3799-3813, 2022, DOI:10.32604/cmc.2022.026131

Abstract Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases. Magnetic resonance imaging (MRI) is a widely utilized tool for the classification and detection of prostate cancer. Since the manual screening process of prostate cancer is difficult, automated diagnostic methods become essential. This study develops a novel Deep Learning based Prostate Cancer Classification (DTL-PSCC) model using MRI images. The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors. In addition, the fuzzy k-nearest neighbour (FKNN) model is utilized for classification process where the… More >

JUAN CARLOS STOCKERT^1,2,*, ALFONSO BLÁZQUEZ-CASTRO³

BIOCELL, Vol.46, No.6, pp. 1391-1415, 2022, DOI:10.32604/biocell.2022.019493

Abstract

Melanins represent one of the most ancient and important group of natural macromolecular pigments. They have multiple biological roles in almost all organisms across the Phyla, examples being photoprotection, anti-oxidative action, radical scavenger activity, and heavy metal removal. From the biomedical point of view, melanocytes are involved in the origin of melanoma tumors, and the main therapeutic advances for their treatment have been revised in Part 1 of this review. The chemical structure of eumelanin is a biological concern of great importance, and therefore, exploring theoretical molecular models and synthesis mechanisms will be here described, as well as molecular orbital… More >

Ahmed S. Almasoud¹, Siwar Ben Haj Hassine², Fahd N. Al-Wesabi^2,3, Mohamed K. Nour⁴, Anwer Mustafa Hilal⁵, Mesfer Al Duhayyim⁶, Manar Ahmed Hamza^5,*, Abdelwahed Motwakel⁵

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5799-5815, 2022, DOI:10.32604/cmc.2022.024556

Abstract Due to the advanced developments of the Internet and information technologies, a massive quantity of electronic data in the biomedical sector has been exponentially increased. To handle the huge amount of biomedical data, automated multi-document biomedical text summarization becomes an effective and robust approach of accessing the increased amount of technical and medical literature in the biomedical sector through the summarization of multiple source documents by retaining the significantly informative data. So, multi-document biomedical text summarization acts as a vital role to alleviate the issue of accessing precise and updated information. This paper presents a Deep Learning based Attention Long… More >

Mahmoud Ragab^1,2,3, Ehab Bahauden Ashary⁴, Maha Farouk S. Sabir⁵, Adel A. Bahaddad⁵, Romany F. Mansour^6,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5441-5457, 2022, DOI:10.32604/cmc.2022.024545

Abstract This study presents a novel method to detect the medical application based on Quantum Computing (QC) and a few Machine Learning (ML) systems. QC has a primary advantage i.e., it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of seconds. So, this model is suggested for medical application only by recent researchers. A novel strategy i.e., Quantum Kernel Method (QKM) is proposed in this paper for data prediction. In this QKM process, Linear Tunicate Swarm Algorithm (LTSA), the optimization technique is used to calculate the loss function initially and is aimed… More >

Esam A. AlQaralleh¹, Halah Nassif², Bassam A. Y. Alqaralleh^2,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5477-5490, 2022, DOI:10.32604/cmc.2022.024364

Abstract Tongue diagnosis is a novel and non-invasive approach commonly employed to carry out the supplementary diagnosis over the globe. Recently, several deep learning (DL) based tongue color image analysis models have existed in the literature for the effective detection of diseases. This paper presents a fusion of handcrafted with deep features based tongue color image analysis (FHDF-TCIA) technique to biomedical applications. The proposed FDHF-TCIA technique aims to investigate the tongue images using fusion model, and thereby determines the existence of disease. Primarily, the FHDF-TCIA technique comprises Gaussian filtering based preprocessing to eradicate the noise. The proposed FHDF-TCIA model encompasses a… More >