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  • Open Access

    Identification of STAT5B as a biomarker for an early diagnosis of endometrial carcinoma


    BIOCELL, Vol.47, No.10, pp. 2283-2300, 2023, DOI:10.32604/biocell.2023.030086

    Abstract Background: The late detection of endometrial carcinoma (EC) at an advanced stage often results in a poor patient prognosis. It is hence important to identify reliable biomarkers to facilitate early detection of EC. Signal transducer and activator of transcription (STAT) family members play an important role in several tumors, however, their impact on EC development and progression remains unclear. Methods: Machine learning methods were used to investigate the importance of STAT5B in EC. Results: Hence, we explored the UALCAN data mining platform and found that while STAT1 and STAT2 were upregulated, STAT5A, STAT5B, and STAT6 were… More >

  • Open Access


    Classification of Brain Tumors Using Hybrid Feature Extraction Based on Modified Deep Learning Techniques

    Tawfeeq Shawly1, Ahmed Alsheikhy2,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 425-443, 2023, DOI:10.32604/cmc.2023.040561

    Abstract According to the World Health Organization (WHO), Brain Tumors (BrT) have a high rate of mortality across the world. The mortality rate, however, decreases with early diagnosis. Brain images, Computed Tomography (CT) scans, Magnetic Resonance Imaging scans (MRIs), segmentation, analysis, and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages. For physicians, diagnosis can be challenging and time-consuming, especially for those with little expertise. As technology advances, Artificial Intelligence (AI) has been used in various domains as a diagnostic tool and offers promising outcomes. Deep-learning techniques are… More >

  • Open Access


    Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

    A. Manju1, R. kaladevi2, Shanmugasundaram Hariharan3, Shih-Yu Chen4,5,*, Vinay Kukreja6, Pradip Kumar Sharma7, Fayez Alqahtani8, Amr Tolba9, Jin Wang10

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 993-1007, 2023, DOI:10.32604/cmc.2023.039567

    Abstract The medical community has more concern on lung cancer analysis. Medical experts’ physical segmentation of lung cancers is time-consuming and needs to be automated. The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques. Computer-Aided Diagnostic (CAD) system aids in the diagnosis and shortens the time necessary to detect the tumor detected. The application of Deep Neural Networks (DNN) has also been exhibited as an excellent and effective method in classification and segmentation tasks. This research aims to separate lung cancers from… More >

  • Open Access


    Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms

    Mujeeb Ur Rehman1,*, Maha Driss2,3, Abdukodir Khakimov4, Sohail Khalid1

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5681-5697, 2022, DOI:10.32604/cmc.2022.025840

    Abstract Lungs are a vital human body organ, and different Obstructive Lung Diseases (OLD) such as asthma, bronchitis, or lung cancer are caused by shortcomings within the lungs. Therefore, early diagnosis of OLD is crucial for such patients suffering from OLD since, after early diagnosis, breathing exercises and medical precautions can effectively improve their health state. A secure non-invasive early diagnosis of OLD is a primordial need, and in this context, digital image processing supported by Artificial Intelligence (AI) techniques is reliable and widely used in the medical field, especially for improving early disease diagnosis. Hence,… More >

  • Open Access


    Early Diagnosis of Alzheimer’s Disease Based on Convolutional Neural Networks

    Atif Mehmood1,*, Ahed Abugabah1, Ahmed Ali AlZubi2, Louis Sanzogni3

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 305-315, 2022, DOI:10.32604/csse.2022.018520

    Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder, causing the most common dementia in the elderly peoples. The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA. Magnetic resonance imaging (MRI) is the leading modality used for the diagnosis of AD. Deep learning based approaches have produced impressive results in this domain. The early diagnosis of AD depends on the efficient use of classification approach. To address this issue, this study proposes a system using two convolutional neural networks (CNN) based approaches for an early diagnosis of… More >

  • Open Access


    Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling

    Omar M. El-Habbak1, Abdelrahman M. Abdelalim1, Nour H. Mohamed1, Habiba M. Abd-Elaty1, Mostafa A. Hammouda1, Yasmeen Y. Mohamed1, Mohanad A. Taifor1, Ali W. Mohamed2,3,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2953-2969, 2022, DOI:10.32604/cmc.2022.020109

    Abstract Parkinson’s disease (PD), one of whose symptoms is dysphonia, is a prevalent neurodegenerative disease. The use of outdated diagnosis techniques, which yield inaccurate and unreliable results, continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field. To solve this issue, the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’ voice recordings. Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers. Results were highly successful, with 90% accuracy produced by the random More >

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