Deep Learning Assisted model for Medical Image Fusion

Accurate prognosis is serious to early detection and successful conduct of non-small cell lung cancer (NSCLC). A deep Q-learning feature representation model that is efficient in order to predict NSCLC is presented in this study. Proposed novel method add adaptive thresholding with many feature extraction methods. By removing distinctive features from the input data, these techniques enable a more complete and accurate depiction of NSCLC characteristics. Usually, these models are not able to increase their classification accuracy with time. To solve these challenges, we offer a 1D convolutional neural network (CNN) with Q-learning, a reinforcement learning method that agrees for continual accuracy increases. The internal algorithms of our proposed model stand out for their reliability and efficiency. Adaptive thresholding allows for precise segmentation of non-small cells by dynamically varying the threshold based on localised pixel intensity. The feature extraction algorithms capture the different spatial and frequency properties found in NSCLC pictures. As a result, the process of feature representation is rich and educational. Furthermore, the model is able to continuously adapt and enhance its classification accuracy thanks to the 1D CNN with Q-learning. A 99.5% effective precision is shown by the experimental results.