IMAGE PATTERN RECOGNITION WITH AN IMPROVISED DEEP LEARNING REGRESSION TECHNIQUE

Advancements in image pattern recognition have revolutionized diverse domains such as healthcare, autonomous systems, and security. Despite these advancements, existing deep learning techniques often encounter challenges in achieving high accuracy, particularly when handling complex image datasets with significant noise or variations. The need for an enhanced approach that balances computational efficiency with superior predictive performance has become critical. This study introduces an Improvised Deep Learning Regression Technique based on InceptionNet for robust image pattern recognition. The proposed method incorporates optimized inception modules with tailored hyperparameter tuning to address limitations in feature extraction and pattern generalization. By employing an adaptive learning rate and advanced regularization mechanisms, the model achieves better performance on large-scale, heterogeneous datasets. The experimental evaluation was conducted using publicly available image datasets, including CIFAR-10 and ImageNet, to ensure comprehensive benchmarking. The results show significant improvements over existing methods. The proposed InceptionNet model achieved an accuracy of 96.5% on the CIFAR-10 dataset and a mean absolute error (MAE) reduction of 15.2% compared to traditional regression techniques. On the ImageNet dataset, the model recorded an accuracy improvement of 7.8% and reduced training time by 12%, validating its computational efficiency. The incorporation of deep inception modules contributed to precise recognition of intricate patterns and subtle variations, making the technique suitable for real-time applications.