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  • Open Access


    Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

    Amani Abdulrahman Albraikan1, Nadhem NEMRI2, Mimouna Abdullah Alkhonaini3, Anwer Mustafa Hilal4,*, Ishfaq Yaseen4, Abdelwahed Motwakel4

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2443-2459, 2023, DOI:10.32604/cmc.2023.026379

    Abstract Melanoma remains a serious illness which is a common form of skin cancer. Since the earlier detection of melanoma reduces the mortality rate, it is essential to design reliable and automated disease diagnosis model using dermoscopic images. The recent advances in deep learning (DL) models find useful to examine the medical image and make proper decisions. In this study, an automated deep learning based melanoma detection and classification (ADL-MDC) model is presented. The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma. The ADL-MDC technique performs contrast enhancement and data augmentation at… More >

  • Open Access


    Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

    R. Poonguzhali1, Sultan Ahmad2, P. Thiruvannamalai Sivasankar3, S. Anantha Babu3, Pranav Joshi4, Gyanendra Prasad Joshi5, Sung Won Kim6,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2179-2194, 2023, DOI:10.32604/cmc.2023.032816

    Abstract Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors (BT). A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate. The location and classification of BTs from huge medicinal images database, obtained from routine medical tasks with manual processes are a higher cost together in effort and time. An automatic recognition, place, and classifier process was desired and useful. This study introduces an Automated Deep Residual U-Net Segmentation with Classification model (ADRU-SCM) for Brain Tumor Diagnosis. The presented ADRU-SCM model majorly focuses on… More >

  • Open Access


    Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

    Mesfer Al Duhayyim1,*, Areej A. Malibari2, Marwa Obayya3, Mohamed K. Nour4, Ahmed S. Salama5, Mohamed I. Eldesouki6, Abu Sarwar Zamani7, Mohammed Rizwanullah7

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5473-5489, 2022, DOI:10.32604/cmc.2022.031976

    Abstract The skeletal bone age assessment (BAA) was extremely implemented in development prediction and auxiliary analysis of medicinal issues. X-ray images of hands were detected from the estimation of bone age, whereas the ossification centers of epiphysis and carpal bones are important regions. The typical skeletal BAA approaches remove these regions for predicting the bone age, however, few of them attain suitable efficacy or accuracy. Automatic BAA techniques with deep learning (DL) methods are reached the leading efficiency on manual and typical approaches. Therefore, this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with… More >

  • Open Access


    Deep Learning Enabled Computer Aided Diagnosis Model for Lung Cancer using Biomedical CT Images

    Mohammad Alamgeer1, Hanan Abdullah Mengash2, Radwa Marzouk2, Mohamed K Nour3, Anwer Mustafa Hilal4,*, Abdelwahed Motwakel4, Abu Sarwar Zamani4, Mohammed Rizwanullah4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1437-1448, 2022, DOI:10.32604/cmc.2022.027896

    Abstract Early detection of lung cancer can help for improving the survival rate of the patients. Biomedical imaging tools such as computed tomography (CT) image was utilized to the proper identification and positioning of lung cancer. The recently developed deep learning (DL) models can be employed for the effectual identification and classification of diseases. This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image, named DLCADLC-BCT technique. The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images. The proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix (GLCM) model for feature… More >

  • Open Access


    Automated Artificial Intelligence Empowered Colorectal Cancer Detection and Classification Model

    Mahmoud Ragab1,2,3,*, Ashwag Albukhari2,4

    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 >

  • Open Access


    Intelligent Deep Transfer Learning Based Malaria Parasite Detection and Classification Model Using Biomedical Image

    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 >

  • Open Access


    Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

    Areej A. Malibari1, Reem Alshahrani2, Fahd N. Al-Wesabi3,*, 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 >

  • Open Access


    Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

    V. Thanikachalam1,*, S. Shanthi2, K. Kalirajan3, Sayed Abdel-Khalek4,5, Mohamed Omri6, Lotfi M. Ladhar7

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5667-5681, 2022, DOI:10.32604/cmc.2022.020965

    Abstract The rapid development of biomedical imaging modalities led to its wide application in disease diagnosis. Tongue-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ubiquitously. Traditionally, physicians examine the characteristics of tongue prior to decision-making. In this scenario, to get rid of qualitative aspects, tongue images can be quantitatively inspected for which a new disease diagnosis model is proposed. This model can reduce the physical harm made to the patients. Several tongue image analytical methodologies have been proposed earlier. However, there is a need exists to design an intelligent Deep Learning (DL) based disease… More >

  • Open Access


    Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model

    Mohamed Ibrahim Waly1, Mohamed Yacin Sikkandar1, Mohamed Abdelkader Aboamer1, Seifedine Kadry2, Orawit Thinnukool3,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3295-3309, 2022, DOI:10.32604/cmc.2022.020713

    Abstract Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases. An important kind of biomedical image is Pap smear image that is widely employed for cervical cancer diagnosis. Cervical cancer is a vital reason for increased women’s mortality rate. Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer. Computer-aided systems for cancerous cell detection need to be developed using deep learning (DL) approaches. This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification (IDCNN-CDC) model using biomedical… More >

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