You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: CNN has built a model of the human brain using the mixture of these networks. CNN layers are organized so that simpler patterns (lines, curves, etc.) are detected initially and more complicated patterns (faces, objects, etc.) are detected afterwards. However, CNN has drawn a lot of interest in data science since it has demonstrated its ability to locate, segment, and identify objects in images. In this study, the term "original CNN architecture" refers to a CNN network and algorithm that are available on Keras or Github. In this study, the CNN algorithm is used exactly as its creators and programmers intended it to be, with no modifications to its processing units, parameterization and hyper-parameter optimization methodologies, design patterns, or layer connections. A well-known CNN network was frequently created and improved by several researchers and programmers over the course of numerous difficulties. Deep learning is a crucial artificial intelligence tool for solving many complicated computer vision problems. Image categorization uses deep learning models, particularly convolutional neural networks (CNNs) problems. Such models work best with lots of data. Because professional clinicians must classify each image, obtaining such a large volume of labeled data for biomedical image classification tasks is expensive and time-consuming. Transfer learning circumvents this issue. This method applies network weights from a model trained on a large dataset to a small dataset problem. Biomedical image categorization often uses CNN models trained on ImageNet, which has over 14 million images. Data scientists have been drawn to the concept of utilizing Deep Convolutional Neural Network (D-CNN) in identifying, classifying, and segmenting brain tumors as a result of the visible benefits of Deep Convolutional Neural Network (D-CNN) in Medical Image Analysis. When it comes to segmenting the timorous region included inside a brain, D-CNN is a set of techniques that has the potential to produce better outcomes when compared to techniques that do not involve deep learning. The multilayered, hierarchical, and block structure of D-CNN allows for the extraction of low-, mid-, and high-level information from pictures of brain tumors. D-CNN shows an outstanding performance in solving the segmentation and classification issues that are based on time and effort consuming tasks like fractionalization of brain tumor cells in Medical Image scans. This is in contrast to the large amount of time and effort that is required for the segmentation process by doctors and radiologists due to the high quantity of data produced by scan centers.

Question: What is difference between CNN and D-CNN?
The paper directly didn't mentioned the difference between CNN and D-CNN. But it is referable from P1 and P2 that, D-CNN multilayered, hierarchical and block-structure compare to CNN.