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  • Open Access

    ARTICLE

    Med-ReLU: A Parameter-Free Hybrid Activation Function for Deep Artificial Neural Network Used in Medical Image Segmentation

    Nawaf Waqas1, Muhammad Islam2,*, Muhammad Yahya3, Shabana Habib4, Mohammed Aloraini2, Sheroz Khan5

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3029-3051, 2025, DOI:10.32604/cmc.2025.064660 - 03 July 2025

    Abstract Deep learning (DL), derived from the domain of Artificial Neural Networks (ANN), forms one of the most essential components of modern deep learning algorithms. DL segmentation models rely on layer-by-layer convolution-based feature representation, guided by forward and backward propagation. A critical aspect of this process is the selection of an appropriate activation function (AF) to ensure robust model learning. However, existing activation functions often fail to effectively address the vanishing gradient problem or are complicated by the need for manual parameter tuning. Most current research on activation function design focuses on classification tasks using natural… More >

  • Open Access

    ARTICLE

    Target Projection Feature Matching Based Deep ANN with LSTM for Lung Cancer Prediction

    Chandrasekar Thaventhiran, K. R. Sekar*

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 495-506, 2022, DOI:10.32604/iasc.2022.019546 - 03 September 2021

    Abstract Prediction of lung cancer at early stages is essential for diagnosing and prescribing the correct treatment. With the continuous development of medical data in healthcare services, Lung cancer prediction is the most concerning area of interest. Therefore, early prediction of cancer helps in reducing the mortality rate of humans. The existing techniques are time-consuming and have very low accuracy. The proposed work introduces a novel technique called Target Projection Feature Matched Deep Artificial Neural Network with LSTM (TPFMDANN-LSTM) for accurate lung cancer prediction with minimum time consumption. The proposed deep learning model consists of multiple… More >

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