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  • Open Access

    ARTICLE

    Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

    Jahanzaib Latif1, Shanshan Tu1,*, Chuangbai Xiao1, Anas Bilal2, Sadaqat Ur Rehman3, Zohaib Ahmad4

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1151-1172, 2023, DOI:10.32604/cmc.2023.040152

    Abstract Glaucoma is a progressive eye disease that can lead to blindness if left untreated. Early detection is crucial to prevent vision loss, but current manual scanning methods are expensive, time-consuming, and require specialized expertise. This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine (EGWO-SVM) method. The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter (AMF) and feature extraction using the previously processed speeded-up robust feature (SURF), histogram of oriented gradients (HOG), and Global features. The enhanced Grey Wolf Optimization (GWO) technique is then employed… More >

  • Open Access

    ARTICLE

    Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases

    Shah Faisal1, Kashif Javed1, Sara Ali1, Areej Alasiry2, Mehrez Marzougui2, Muhammad Attique Khan3,*, Jae-Hyuk Cha4,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 895-914, 2023, DOI:10.32604/cmc.2023.039781

    Abstract Citrus fruit crops are among the world’s most important agricultural products, but pests and diseases impact their cultivation, resulting in yield and quality losses. Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade, allowing for early disease detection and improving agricultural production. This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning (DL) model, which improved accuracy while decreasing computational complexity. The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy. Using… More >

  • Open Access

    ARTICLE

    Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

    Ali Haider Khan1,2,*, Hassaan Malik2, Wajeeha Khalil3, Sayyid Kamran Hussain4, Tayyaba Anees5, Muzammil Hussain2

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 133-150, 2023, DOI:10.32604/cmc.2023.039518

    Abstract To prevent irreversible damage to one’s eyesight, ocular diseases (ODs) need to be recognized and treated immediately. Color fundus imaging (CFI) is a screening technology that is both effective and economical. According to CFIs, the early stages of the disease are characterized by a paucity of observable symptoms, which necessitates the prompt creation of automated and robust diagnostic algorithms. The traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of eyes. In addition, they usually only target one or a few different… 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

    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 deep-learning models and a classifier… More >

  • Open Access

    ARTICLE

    Alzheimer’s Disease Stage Classification Using a Deep Transfer Learning and Sparse Auto Encoder Method

    Deepthi K. Oommen*, J. Arunnehru

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 793-811, 2023, DOI:10.32604/cmc.2023.038640

    Abstract Alzheimer’s Disease (AD) is a progressive neurological disease. Early diagnosis of this illness using conventional methods is very challenging. Deep Learning (DL) is one of the finest solutions for improving diagnostic procedures’ performance and forecast accuracy. The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups. In light of research investigations, it is vital to consider age as one of the key criteria when choosing the subjects. The younger subjects are more susceptible to the perishable side than the older onset. The proposed investigation concentrated on the younger onset. The research used… More >

  • Open Access

    ARTICLE

    A Novel Multi-Stage Bispectral Deep Learning Method for Protein Family Classification

    Amjed Al Fahoum*, Ala’a Zyout, Hiam Alquran, Isam Abu-Qasmieh

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1173-1193, 2023, DOI:10.32604/cmc.2023.038304

    Abstract Complex proteins are needed for many biological activities. Folding amino acid chains reveals their properties and functions. They support healthy tissue structure, physiology, and homeostasis. Precision medicine and treatments require quantitative protein identification and function. Despite technical advances and protein sequence data exploration, bioinformatics’ “basic structure” problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains unsolved. Protein function inference from amino acid sequences is the main biological data challenge. This study analyzes whether raw sequencing can characterize biological facts. A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.… More >

  • Open Access

    ARTICLE

    Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

    Muhammad Irfan1, Ahmad Shaf2,*, Tariq Ali2, Umar Farooq2, Saifur Rahman1, Salim Nasar Faraj Mursal1, Mohammed Jalalah1, Samar M. Alqhtani3, Omar AlShorman4

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 711-729, 2023, DOI:10.32604/cmc.2023.038176

    Abstract A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated… More >

  • Open Access

    ARTICLE

    Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

    Hadeel Alsolai1, Mohammed Rizwanullah2,*, Mashael Maashi3, Mahmoud Othman4, Amani A. Alneil2, Amgad Atta Abdelmageed2

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 975-992, 2023, DOI:10.32604/cmc.2023.037936

    Abstract Soil classification is one of the emanating topics and major concerns in many countries. As the population has been increasing at a rapid pace, the demand for food also increases dynamically. Common approaches used by agriculturalists are inadequate to satisfy the rising demand, and thus they have hindered soil cultivation. There comes a demand for computer-related soil classification methods to support agriculturalists. This study introduces a Gradient-Based Optimizer and Deep Learning (DL) for Automated Soil Classification (GBODL-ASC) technique. The presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision approaches. In the presented GBODL-ASC technique, three major… More >

  • Open Access

    ARTICLE

    Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation

    Mona Jamjoom1, Ahmed Elhadad2, Hussein Abulkasim3,*, Safia Abbas4

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 367-382, 2023, DOI:10.32604/cmc.2023.037310

    Abstract Several pests feed on leaves, stems, bases, and the entire plant, causing plant illnesses. As a result, it is vital to identify and eliminate the disease before causing any damage to plants. Manually detecting plant disease and treating it is pretty challenging in this period. Image processing is employed to detect plant disease since it requires much effort and an extended processing period. The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases, including Phytophthora infestans, Fusarium graminearum,… More >

  • Open Access

    ARTICLE

    Tackling Faceless Killers: Toxic Comment Detection to Maintain a Healthy Internet Environment

    Semi Park, Kyungho Lee*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 813-826, 2023, DOI:10.32604/cmc.2023.035313

    Abstract According to BBC News, online hate speech increased by 20% during the COVID-19 pandemic. Hate speech from anonymous users can result in psychological harm, including depression and trauma, and can even lead to suicide. Malicious online comments are increasingly becoming a social and cultural problem. It is therefore critical to detect such comments at the national level and detect malicious users at the corporate level. To achieve a healthy and safe Internet environment, studies should focus on institutional and technical topics. The detection of toxic comments can create a safe online environment. In this study, to detect malicious comments, we… More >

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