Work place: Department of Information Technology, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India
E-mail: mblessa@mepcoeng.ac.in
Website:
Research Interests: Machine Learning
Biography
Blessa Binolin Pepsi M., received her B.Tech degree in Information Technology from Institute of Road and Transport Technology, Erode, Tamilnadu, India in 2010, M.E. degree in Computer Science and Engineering from Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India in 2012. Her research interests include machine learning and big data analytics. She is currently working as Assistant Professor (Selection Grade), with the Department of Information Technology, in Mepco Schlenk Engineering College, Sivakasi, India and pursuing PhD as part-time research scholar under Anna University, Chennai.
By Blessa Binolin Pepsi M. Anandhi H. Karunyaharini S. Visali N.
DOI: https://doi.org/10.5815/ijitcs.2025.05.05, Pub. Date: 8 Oct. 2025
In the healthcare field, the detection of critical diseases such as brain tumors is essential. A technique like traditional support vector machine has been commonly used for brain tumor classification. However, Processing and detecting brain tumors requires achieving high accuracy with shorter detection time and reduced complexity. To accomplish this, efficient feature selection is necessary, which can be based on various factors. A convolutional neural network-based stacking technique is introduced for effective brain tumor classification and prediction using Red Panda optimization. By efficiently extracting spatial data from medical images, a convolutional neural network is used in stacking to enhance thecapacity of our model for abnormality detection and classification in the prediction of brain tumors. Red panda optimization is a biologically inspired stochastic optimization algorithm used for the effective selection of significant features. This Technique improves the prediction accuracy in a shorter period and reduces the complexity by selecting significant features for a huge amount of data by employing effective optimization. This technique is tested on multiple standard datasets to assess our model’s performance. Our technique is compared to other optimization models such as Mutual information-based optimization and traditional particle swarm optimization for further validation. Our model showed an improvement in detection accuracy to 98% with a better reduction in detection time and complexity.
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