A Mathematical Approach to Convolutional Neural Networks for Specific Application using Deep CNN Model

Abstract

A novel DCNN-based model tailored for specific problem or application, such as image classification, medical diagnosis, etc. By integrating advanced mathematical techniques with convolution operations, we enhance the network's efficiency, accuracy, and interpretability. After the CNN preparing process, the met channel loads characterize a bunch of anchor vectors in the DCNN model. Anchor vectors address the as often as possible happening designs (or the unearthly parts). The need of correction is made sense of utilizing the DCNN model. Then, at that point, the way of behaving of a two-layer DCNN framework is broke down and contrasted and its one-layer partner. The LeNet-5 and the MNIST dataset are utilized to outline conversation focuses. We explore key mathematical concepts, such as convolution operations, optimization strategies, and regularization techniques, to improve model performance. Our performance analyses demonstrate the better execution of our strategy looked at than existing CNN architectures in terms of metrics: accuracy, F1-score, etc.