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Search Results (18)
  • Open Access

    ARTICLE

    Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification

    K. Shankar1, E. Laxmi Lydia2, Sachin Kumar1,*, Ali S. Abosinne3, Ahmed alkhayyat4, A. H. Abbas5, Sarmad Nozad Mahmood6

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4541-4557, 2022, DOI:10.32604/cmc.2022.031247 - 28 July 2022

    Abstract Oral Squamous Cell Carcinoma (OSCC) is a type of Head and Neck Squamous Cell Carcinoma (HNSCC) and it should be diagnosed at early stages to accomplish efficient treatment, increase the survival rate, and reduce death rate. Histopathological imaging is a wide-spread standard used for OSCC detection. However, it is a cumbersome process and demands expert’s knowledge. So, there is a need exists for automated detection of OSCC using Artificial Intelligence (AI) and Computer Vision (CV) technologies. In this background, the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification… More >

  • Open Access

    ARTICLE

    Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

    Thavavel Vaiyapuri1, S. Srinivasan2, Mohamed Yacin Sikkandar3, T. S. Balaji4,5, Seifedine Kadry6, Maytham N. Meqdad7, Yunyoung Nam8,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5543-5557, 2022, DOI:10.32604/cmc.2022.023919 - 28 July 2022

    Abstract In past decades, retinal diseases have become more common and affect people of all age grounds over the globe. For examining retinal eye disease, an artificial intelligence (AI) based multilabel classification model is needed for automated diagnosis. To analyze the retinal malady, the system proposes a multiclass and multi-label arrangement method. Therefore, the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge, which tends to be time-consuming, vulnerable generalization ability, and unfeasible in massive datasets. Therefore, the automated diagnosis of multi-retinal diseases becomes essential, which can be solved… More >

  • Open Access

    ARTICLE

    Deer Hunting Optimization with Deep Learning Model for Lung Cancer Classification

    Mahmoud Ragab1,2,3,*, Hesham A. Abdushkour4, Alaa F. Nahhas5, Wajdi H. Aljedaibi6

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 533-546, 2022, DOI:10.32604/cmc.2022.028856 - 18 May 2022

    Abstract Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced stages. Early recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare sector. Computer aided diagnosis (CAD) models can be designed to effectually identify and classify the existence of lung cancer using medical images. The recently developed deep learning (DL) models find a way for accurate lung nodule classification process. Therefore, this article presents a deer hunting optimization with deep convolutional neural network… More >

  • Open Access

    ARTICLE

    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 - 18 May 2022

    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 More >

  • Open Access

    ARTICLE

    Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

    Yasmeen Al-Saeed1,2, Wael A. Gab-Allah1, Hassan Soliman1, Maysoon F. Abulkhair3, Wafaa M. Shalash4, Mohammed Elmogy1,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4871-4894, 2022, DOI:10.32604/cmc.2022.023638 - 14 January 2022

    Abstract One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main More >

  • Open Access

    ARTICLE

    Lung Nodule Detection Based on YOLOv3 Deep Learning with Limited Datasets

    Zhaohui Bu1, Xuejun Zhang2,3,*, Jianxiang Lu4, Huan Lao5, Chan Liang2, Xianfu Xu2, Yini Wei2, Hongjie Zeng2

    Molecular & Cellular Biomechanics, Vol.19, No.1, pp. 17-28, 2022, DOI:10.32604/mcb.2022.018318 - 12 January 2022

    Abstract The early symptom of lung tumor is always appeared as nodule on CT scans, among which 30% to 40% are malignant according to statistics studies. Therefore, early detection and classification of lung nodules are crucial to the treatment of lung cancer. With the increasing prevalence of lung cancer, large amount of CT images waiting for diagnosis are huge burdens to doctors who may missed or false detect abnormalities due to fatigue. Methods: In this study, we propose a novel lung nodule detection method based on YOLOv3 deep learning algorithm with only one preprocessing step is… More >

  • Open Access

    ARTICLE

    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 - 27 September 2021

    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… More >

  • Open Access

    ARTICLE

    Big Data Analytics with OENN Based Clinical Decision Support System

    Thejovathi Murari1, L. Prathiba2, Kranthi Kumar Singamaneni3,*, D. Venu4, Vinay Kumar Nassa5, Rachna Kohar6, Satyajit Sidheshwar Uparkar7

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1241-1256, 2022, DOI:10.32604/iasc.2022.020203 - 22 September 2021

    Abstract In recent times, big data analytics using Machine Learning (ML) possesses several merits for assimilation and validation of massive quantity of complicated healthcare data. ML models are found to be scalable and flexible over conventional statistical tools, which makes them suitable for risk stratification, diagnosis, classification and survival prediction. In spite of these benefits, the utilization of ML in healthcare sector faces challenges which necessitate massive training data, data preprocessing, model training and parameter optimization based on the clinical problem. To resolve these issues, this paper presents new Big Data Analytics with Optimal Elman Neural… More >

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