You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Deep Learning Models for Retinal Blood Vessels Segmentation: A Review 
Chapter Title:  III. CONVOLUTIONAL NEURAL NETWORKS (CNNs) 
1. Abstract of Cited Paper (BIB001):  We present an application of back-propagation networks to handwritten digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated digits. The method has 1% error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service. 
2. Abstract of Cited Paper (BIB002):  The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond. 
3. Abstract of Cited Paper (BIB003):  Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark Krizhevsky et al. [18]. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we explore both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark. We also perform an ablation study to discover the performance contribution from different model layers. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets. 
4. Abstract of Cited Paper (BIB004):  In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. 
5. Abstract of Cited Paper (BIB005):  We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection. 
6. Abstract of Cited Paper (BIB006):  Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Literature Review:
Convolutional Neural Networks are biologically-inspired of multi-layer perceptrons (MLPs). They are well-known deep learning architectures inspired by the natural visual perception mechanism of the living creatures. The history of CNNs started with the experiments conducted by Hubel and Wiesel in 1959 BIB001 . They found that the cells in animals' visual cortex are responsible for detecting the light in the receptive fields. In 1990, LeCun et al. BIB002 published a paper where they described the modern framework of CNN. They introduced a neural network called LeNet-5 which is used to classify the handwriting digits. They used backpropagation algorithm to train the neural network. However, due to the lack of data and the computation power at that time, the proposed networks couldn't perform well in large-scale problems. After that, many researchers have developed methods to overcome some problems encountered in training deep CNNs. In 2012, Krizhevsky et al.  proposed a deep CNN VOLUME 7, 2019 FIGURE 6. LeNet-5 architecture BIB002 . architecture to solve the problem of image classification. They showed significant improvements and outperformed results upon previous methods. They won the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) BIB003 . The proposed architecture is similar to LeNet-5 but with a deeper structure, and after that it was known as AlexNet. With the success of AlexNet, many CNNs architecture have been proposed to improve the performance and get more accurate results such as ZFNet BIB004 , VGGNet BIB005 , GoogleNet BIB006 , and ResNet BIB007 . ResNet is the deepest architecture and it is 20 times deeper than AlexNet and 8 times deeper than VGGNet. It is observed that deeper architectures are better for the classification tasks. The reason behind this is that the network can approximate the output because of the increasing of the nonlinearity and extracting more representative features.