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  • Open Access


    Modeling a Novel Hyper-Parameter Tuned Deep Learning Enabled Malaria Parasite Detection and Classification

    Tamal Kumar Kundu1, Dinesh Kumar Anguraj1,*, S. V. Sudha2,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3289-3304, 2023, DOI:10.32604/cmc.2023.039515

    Abstract A theoretical methodology is suggested for finding the malaria parasites’ presence with the help of an intelligent hyper-parameter tuned Deep Learning (DL) based malaria parasite detection and classification (HPTDL-MPDC) in the smear images of human peripheral blood. Some existing approaches fail to predict the malaria parasitic features and reduce the prediction accuracy. The trained model initiated in the proposed system for classifying peripheral blood smear images into the non-parasite or parasite classes using the available online dataset. The Adagrad optimizer is stacked with the suggested pre-trained Deep Neural Network (DNN) with the help of the… More >

  • Open Access


    Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

    Ibrar Amin1, Saima Hassan1, Samir Brahim Belhaouari2,*, Muhammad Hamza Azam3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6335-6349, 2023, DOI:10.32604/cmc.2023.033860

    Abstract Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are time-consuming that require a great deal of human expertise and efforts. Computer-based automated diagnosis of diseases is progressively becoming popular. Although deep learning models show high performance in the medical field, it demands a large volume of data for training which is hard to acquire for medical problems. Similarly, labeling of medical images can be done with the help of medical experts only. Several recent studies have utilized deep learning models to develop efficient malaria diagnostic More >

  • Open Access


    Numerical Investigation of Malaria Disease Dynamics in Fuzzy Environment

    Fazal Dayan1,*, Dumitru Baleanu2,3,4, Nauman Ahmed5, Jan Awrejcewicz6, Muhammad Rafiq7, Ali Raza8, Muhammad Ozair Ahmad5

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2345-2361, 2023, DOI:10.32604/cmc.2023.033261

    Abstract The application of fuzzy theory is vital in all scientific disciplines. The construction of mathematical models with fuzziness is little studied in the literature. With this in mind and for a better understanding of the disease, an SEIR model of malaria transmission with fuzziness is examined in this study by extending a classical model of malaria transmission. The parameters and , being function of the malaria virus load, are considered fuzzy numbers. Three steady states and the reproduction number of the model are analyzed in fuzzy senses. A numerical technique is developed in a fuzzy More >

  • Open Access


    A Novel Convolutional Neural Network Model for Malaria Cell Images Classification

    Esraa Hassan1,3,*, Mahmoud Y. Shams1, Noha A. Hikal2, Samir Elmougy3

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5889-5907, 2022, DOI:10.32604/cmc.2022.025629

    Abstract Infectious diseases are an imminent danger that faces human beings around the world. Malaria is considered a highly contagious disease. The diagnosis of various diseases, including malaria, was performed manually, but it required a lot of time and had some human errors. Therefore, there is a need to investigate an efficient and fast automatic diagnosis system. Deploying deep learning algorithms can provide a solution in which they can learn complex image patterns and have a rapid improvement in medical image analysis. This study proposed a Convolutional Neural Network (CNN) model to detect malaria automatically. A 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… More >

  • Open Access


    An Intelligent Classification System for Trophozoite Stages in Malaria Species

    Siti Nurul Aqmariah Mohd Kanafiah1,*, Mohd Yusoff Mashor1, Zeehaida Mohamed2, Yap Chun Way1, Shazmin Aniza Abdul Shukor1, Yessi Jusman3

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 687-697, 2022, DOI:10.32604/iasc.2022.024361

    Abstract Malaria is categorised as a dangerous disease that can cause fatal in many countries. Therefore, early detection of malaria is essential to get rapid treatment. The malaria detection process is usually carried out with a 100x magnification of thin blood smear using microscope observation. However, the microbiologist required a long time to identify malaria types before applying any proper treatment to the patient. It also has difficulty to differentiate the species in trophozoite stages because of similar characteristics between species. To overcome these problems, a computer-aided diagnosis system is proposed to classify trophozoite stages of PlasmodiumMore >

  • Open Access


    Malaria Parasite Detection Using a Quantum-Convolutional Network

    Javaria Amin1 , Muhammad Almas Anjum2 , Abida Sharif3 , Mudassar Raza4 , Seifedine Kadry5, Yunyoung Nam6,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6023-6039, 2022, DOI:10.32604/cmc.2022.019115


    Malaria is a severe illness triggered by parasites that spreads via mosquito bites. In underdeveloped nations, malaria is one of the top causes of mortality, and it is mainly diagnosed through microscopy. Computer-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected groups. Therefore, in this study, we present a new idea based on the ensemble quantum-classical framework for malaria classification. The methods comprise three core steps: localization, segmentation, and classification. In the first core step, an improved FRCNN model is proposed for the localization of the infected

    More >

  • Open Access

    Malaria Blood Smear Classification Using Deep Learning and Best Features Selection

    Talha Imran1, Muhammad Attique Khan2, Muhammad Sharif1, Usman Tariq3, Yu-Dong Zhang4, Yunyoung Nam5,*, Yunja Nam5, Byeong-Gwon Kang5

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1875-1891, 2022, DOI:10.32604/cmc.2022.018946

    Abstract Malaria is a critical health condition that affects both sultry and frigid region worldwide, giving rise to millions of cases of disease and thousands of deaths over the years. Malaria is caused by parasites that enter the human red blood cells, grow there, and damage them over time. Therefore, it is diagnosed by a detailed examination of blood cells under the microscope. This is the most extensively used malaria diagnosis technique, but it yields limited and unreliable results due to the manual human involvement. In this work, an automated malaria blood smear classification model is More >

  • Open Access


    Imperative Dynamic Routing Between Capsules Network for Malaria Classification

    G. Madhu1,*, A. Govardhan2, B. Sunil Srinivas3, Kshira Sagar Sahoo4, N. Z. Jhanjhi5, K. S. Vardhan1, B. Rohit6

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 903-919, 2021, DOI:10.32604/cmc.2021.016114

    Abstract Malaria is a severe epidemic disease caused by Plasmodium falciparum. The parasite causes critical illness if persisted for longer durations and delay in precise treatment can lead to further complications. The automatic diagnostic model provides aid for medical practitioners to avail a fast and efficient diagnosis. Most of the existing work either utilizes a fully connected convolution neural network with successive pooling layers which causes loss of information in pixels. Further, convolutions can capture spatial invariances but, cannot capture rotational invariances. Hence to overcome these limitations, this research, develops an Imperative Dynamic routing mechanism with fully… More >

  • Open Access


    Numerical Control Measures of Stochastic Malaria Epidemic Model

    Muhammad Rafiq1, Ali Ahmadian2, *, Ali Raza3, Dumitru Baleanu4, Muhammad Sarwar Ahsan1, Mohammad Hasan Abdul Sathar5

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 33-51, 2020, DOI:10.32604/cmc.2020.010893

    Abstract Nonlinear stochastic modeling has significant role in the all discipline of sciences. The essential control measuring features of modeling are positivity, boundedness and dynamical consistency. Unfortunately, the existing stochastic methods in literature do not restore aforesaid control measuring features, particularly for the stochastic models. Therefore, these gaps should be occupied up in literature, by constructing the control measuring features numerical method. We shall present a numerical control measures for stochastic malaria model in this manuscript. The results of the stochastic model are discussed in contrast of its equivalent deterministic model. If the basic reproduction number More >

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