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  • Open Access

    ARTICLE

    Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

    Nahed Tawfik1,*, Heba M. Emara2, Walid El-Shafai3, Naglaa F. Soliman4, Abeer D. Algarni4, Fathi E. Abd El-Samie4

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 271-307, 2024, DOI:10.32604/cmc.2024.052404 - 15 October 2024

    Abstract Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins, including hereditary factors and various clinical changes. It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally. Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately, leading to improved prognosis and higher survival rates. The significant increase in both the incidence and mortality rates of lung cancer, particularly its ranking as the second most prevalent cancer among women worldwide, underscores the need for comprehensive research into efficient… More >

  • Open Access

    ARTICLE

    Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques

    Raveena Selvanarayanan1, Surendran Rajendran1,*, Youseef Alotaibi2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 759-782, 2024, DOI:10.32604/cmes.2023.044084 - 30 December 2023

    Abstract Colletotrichum kahawae (Coffee Berry Disease) spreads through spores that can be carried by wind, rain, and insects affecting coffee plantations, and causes 80% yield losses and poor-quality coffee beans. The deadly disease is hard to control because wind, rain, and insects carry spores. Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93% accuracy using a random forest method. If the dataset is too small and noisy, the algorithm may not learn data patterns and generate accurate predictions.… More >

  • Open Access

    ARTICLE

    Early detection of breast cancer in mammograms using the lightweight modification of efficientNet B3

    Nabilah Ruza1, Saiful Izzuan Hussain2, Siti Kamariah Che Mohamed3, Mohd Hafiz Arzmi4,5

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.39, No.3, pp. 1-7, 2023, DOI:10.23967/j.rimni.2023.08.002 - 01 September 2023

    Abstract Breast cancer is one of the leading causes of death in women worldwide and early detection is critical to improving survival rates. In this study, we present a modified deep learning method for automatic feature detection for breast mass classification on mammograms. We propose to use EfficientNet, a Convolutional Neural Network (CNN) architecture that requires minimal parameters. The main advantage of EfficientNet is the small number of parameters, which allows efficient and accurate classification of mammogram images. Our experiments show that EfficientNet, with an overall accuracy of 86.5 percent, has the potential to be the More >

  • Open Access

    ARTICLE

    Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier

    S M Hasan Mahmud1,2, Md Mamun Ali3, Mohammad Fahim Shahriar1, Fahad Ahmed Al-Zahrani4, Kawsar Ahmed5,6,*, Dip Nandi1, Francis M. Bui5

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3933-3948, 2023, DOI:10.32604/cmc.2023.039020 - 08 October 2023

    Abstract Alzheimer’s disease (AD) is a neurodevelopmental impairment that results in a person’s behavior, thinking, and memory loss. The most common symptoms of AD are losing memory and early aging. In addition to these, there are several serious impacts of AD. However, the impact of AD can be mitigated by early-stage detection though it cannot be cured permanently. Early-stage detection is the most challenging task for controlling and mitigating the impact of AD. The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to… More >

  • Open Access

    ARTICLE

    Development of Features for Early Detection of Defects and Assessment of Bridge Decks

    Ahmed Silik1,2,7, Xiaodong Wang3, Chenyue Mei3, Xiaolei Jin3, Xudong Zhou4, Wei Zhou4, Congning Chen4, Weixing Hong1,2, Jiawei Li1,2, Mingjie Mao1,2, Yuhan Liu1,2, Mohammad Noori5,6,*, Wael A. Altabey8,*

    Structural Durability & Health Monitoring, Vol.17, No.4, pp. 257-281, 2023, DOI:10.32604/sdhm.2023.023617 - 02 August 2023

    Abstract Damage detection is an important area with growing interest in mechanical and structural engineering. One of the critical issues in damage detection is how to determine indices sensitive to the structural damage and insensitive to the surrounding environmental variations. Current damage identification indices commonly focus on structural dynamic characteristics such as natural frequencies, mode shapes, and frequency responses. This study aimed at developing a technique based on energy Curvature Difference, power spectrum density, correlation-based index, load distribution factor, and neutral axis shift to assess the bridge deck condition. In addition to tracking energy and frequency More > Graphic Abstract

    Development of Features for Early Detection of Defects and Assessment of Bridge Decks

  • Open Access

    ARTICLE

    An Artificial Intelligence Algorithm for the Real-Time Early Detection of Sticking Phenomena in Horizontal Shale Gas Wells

    Qing Wang*, Haige Wang, Hongchun Huang, Lubin Zhuo, Guodong Ji

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2569-2578, 2023, DOI:10.32604/fdmp.2023.025349 - 25 June 2023

    Abstract Sticking is the most serious cause of failure in complex drilling operations. In the present work a novel “early warning” method based on an artificial intelligence algorithm is proposed to overcome some of the known problems associated with existing sticking-identification technologies. The method is tested against a practical case study (Southern Sichuan shale gas drilling operations). It is shown that the twelve sets of sticking fault diagnostic results obtained from a simulation are all consistent with the actual downhole state; furthermore, the results from four groups of verification samples are also consistent with the actual More >

  • Open Access

    ARTICLE

    An Improved Fully Automated Breast Cancer Detection and Classification System

    Tawfeeq Shawly1, Ahmed A. Alsheikhy2,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 731-751, 2023, DOI:10.32604/cmc.2023.039433 - 08 June 2023

    Abstract More than 500,000 patients are diagnosed with breast cancer annually. Authorities worldwide reported a death rate of 11.6% in 2018. Breast tumors are considered a fatal disease and primarily affect middle-aged women. Various approaches to identify and classify the disease using different technologies, such as deep learning and image segmentation, have been developed. Some of these methods reach 99% accuracy. However, boosting accuracy remains highly important as patients’ lives depend on early diagnosis and specified treatment plans. This paper presents a fully computerized method to detect and categorize tumor masses in the breast using two… More >

  • Open Access

    ARTICLE

    Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

    Hala Ahmed1, Hassan Soliman1, Shaker El-Sappagh2,3,4, Tamer Abuhmed4,*, Mohammed Elmogy1

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2773-2795, 2023, DOI:10.32604/csse.2023.036371 - 03 April 2023

    Abstract The precise diagnosis of Alzheimer’s disease is critical for patient treatment, especially at the early stage, because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage occurs. It is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities, known as image fusion. In this paper, the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain images. First, the preprocessing was performed on the data. Then, the data augmentation techniques are used to handle overfitting. Also, the… More >

  • Open Access

    ARTICLE

    Early Detection Glaucoma and Stargardt’s Disease Using Deep Learning Techniques

    Somasundaram Devaraj*, Senthil Kumar Arunachalam

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1283-1299, 2023, DOI:10.32604/iasc.2023.033200 - 05 January 2023

    Abstract Retinal fundus images are used to discover many diseases. Several Machine learning algorithms are designed to identify the Glaucoma disease. But the accuracy and time consumption performance were not improved. To address this problem Max Pool Convolution Neural Kuan Filtered Tobit Regressive Segmentation based Radial Basis Image Classifier (MPCNKFTRS-RBIC) Model is used for detecting the Glaucoma and Stargardt’s disease by early period using higher accuracy and minimal time. In MPCNKFTRS-RBIC Model, the retinal fundus image is considered as an input which is preprocessed in hidden layer 1 using weighted adaptive Kuan filter. Then, preprocessed retinal… More >

  • Open Access

    ARTICLE

    Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

    Meshal Alharbi, Shabana R. Ziyad*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5483-5505, 2023, DOI:10.32604/cmc.2023.032341 - 28 December 2022

    Abstract Aging is a natural process that leads to debility, disease, and dependency. Alzheimer’s disease (AD) causes degeneration of the brain cells leading to cognitive decline and memory loss, as well as dependence on others to fulfill basic daily needs. AD is the major cause of dementia. Computer-aided diagnosis (CADx) tools aid medical practitioners in accurately identifying diseases such as AD in patients. This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop (IWD) algorithm and the Random Forest (RF) classifier. The IWD algorithm an efficient feature… More >

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