Designing algorithm for context based analysis using deep learning (CNN + RNN) with image dataset

Deep learning (DL) algorithms may be used to vast amounts of data to simplify and improve the scientific method of automatic decision-making activities rather than depending on human encoded heuristics. The combination of DL methods has resulted in substantial breakthroughs in the area of computer vision in recent years. This research introduces novel feature reduction strategies and offers an enhanced classification in our suggested image retrieval search region. In this study, we present a new approach for context-based image analysis by combining the strengths of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The suggested form is meant to capture spatial and temporal connections within image sequences, allowing for a fuller comprehension of the larger context. Initially the experimental datasets were gathered. The significance of a representative and varied dataset for testing and training should be emphasized. The steps done in preprocessing to get the data prepared for analysis. Introduce the idea of using a bag of words (BOW) to express characteristics in an image. As part of the extraction stages, we use a feature extraction approach, in this case a Gabor filters technique, to condense the feature space without compromising classification accuracy. Next, our suggested network approach (CNN + RNN) is used to classify the images. The experimental findings, such as the F1-score, recall, accuracy, and precision are demonstrated and the performance of the suggested algorithm is evaluated against the cutting-edge techniques. Due to the intricate network design, processing components, and characteristics of the underlying datasets, CNN + RNN with images provided superior classification accuracy.