Integration Of Medical Image Processing Techniques In Deeper Neural Networks With Artificial Intelligence

Abstract

The interpretation and communication of image meaning may be achieved through the utilization of techniques derived from both natural language processing and computer vision. Can a machine replicate the ability of the human brain to offer a comprehensive depiction of an image.The task of captioning photographs is widely acknowledged as a challenging endeavor within the field of artificial intelligence. The process of transforming an image into grammatically accurate text necessitates the utilization of both natural language processing and computer vision techniques. The advancement of deep learning methodologies and the abundance of publicly accessible datasets have facilitated the development of diverse models for the automated generation of image descriptions. The initial stage in generating a satisfactory description of an input image is to classify it based on the highest number of objects present. By using concepts from Natural Language Processing (NLP) and a neural network, we may do this. This paper provides a comprehensive explanation of the integration of Long Short-Term Memory with a "Convolutional Neural Network" in order to generate a visual representation. The classification of pictures and text is facilitated by the utilization of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). In order to enhance the accuracy of its predictions, we trained the model to utilize a larger lexical vocabulary when characterizing the photos it has encountered. After conducting several trials on various photo datasets, we have shown that visual description is the primary determinant of a model's accuracy. Typically, the result enhances as the size of the dataset increases.