ENHANCED IMAGE RECOGNITION WITH INTERPRETABLE SOFT COMPUTING: A HYBRID MODEL USING GENETIC ALGORITHMS, CNN, AND FUZZY LOGIC ON THE COCO DATASET
Mandlik G. G, Lokhande S. N · INTERNATIONAL JOURNAL OF GRAPHICS AND MULTIMEDIA · 2025
Image recognition continues to be a critical task in computer vision yet achieving high accuracy while maintaining interpretability remains a challenge.Traditional deep learning models often rely heavily on extensive training data and lack explainability in decision-making.Moreover, selecting optimal features from high-dimensional image data is computationally intensive and prone to redundancy, affecting overall model efficiency and accuracy.To address these challenges, this study presents a novel approach by integrating various advanced techniques such as-Genetic Algorithm (GA)-based feature selection, Convolutional Neural Networks (CNN) for robust feature extraction, and a Feedforward Neural Network (FNN) for classification.A fuzzy logic decision module is employed to enhance interpretability and improve the final decisionmaking process.The methodology is implemented on the COCO dataset, which Mandlik G. G, Lokhande S. N https://iaeme.com/Home