Object Detection with Convolutional Neural Networks

Li, Kaidong; Ma, Wenchi; Sajid, Usman; Wu, Yuanwei; Wang, Guanghui

doi:10.1201/9781351003827-2

Cited by 36 publications

(22 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning models: Inspired by the success of AlexNet [16] in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012, convolutional neural networks (CNN) have attracted a lot of attention and been successfully applied to image classification [20][21][22], object detection [4,23,24], depth estimation [25,26], image transformation [27,28], and crowd counting [29]citesajid2020plug. VGGNets [14], and GoogleNet [17], the ILSVRC winners of 2014 and 2015, proved that deeper models could significantly increase the ability of representations.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A comparative study on polyp classification using convolutional neural networks

Patel

Tao

et al. 2020

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Colorectal cancer is the third most common cancer diagnosed in both men and women in the United States. Most colorectal cancers start as a growth on the inner lining of the colon or rectum, called 'polyp'. Not all polyps are cancerous, but some can develop into cancer. Early detection and recognition of the type of polyps is critical to prevent cancer and change outcomes. However, visual classification of polyps is challenging due to varying illumination conditions of endoscopy, variant texture, appearance, and overlapping morphology between polyps. More importantly, evaluation of polyp patterns by gastroenterologists is subjective leading to a poor agreement among observers. Deep convolutional neural networks have proven very successful in object classification across various object categories. In this work, we compare the performance of the state-of-the-art general object classification models for polyp classification. We trained a total of six CNN models end-to-end using a dataset of 157 video sequences composed of two types of polyps: hyperplastic and adenomatous. Our results demonstrate that the state-of-the-art CNN models can successfully classify polyps with an accuracy comparable or better than reported among gastroenterologists. The results of this study can guide future research in polyp classification.

show abstract

Section: Related Workmentioning

confidence: 99%

“…In recent years, deep learning algorithms have shown their outstanding performance on various generic datasets [4]. In some computer vision tasks, including strategic board games, Atari games, and generic object recognition, deep learning even outperforms human accuracy.…”

Section: Introductionmentioning

confidence: 99%

A comparative study on polyp classification using convolutional neural networks

Patel

Tao

et al. 2020

PLoS ONE

Self Cite

View full text Add to dashboard Cite

show abstract

“…DNNs have essentially revolutionized the problem-solving approaches in computer vision and natural language processing (NLP) fields [ 4 , 5 ]. For example, CNNs have dominated image recognition [ 6 ], object detection [ 7–9 ] and activity recognition [ 10 ]. RNNs have been widely applied to text mining and machine translation [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Interpretation of deep learning in genomics and epigenomics

Talukder

Barham

et al. 2020

Briefings in Bioinformatics

View full text Add to dashboard Cite

Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies towards understanding the underlying molecular and cellular mechanisms. Deep neural networks (DNNs) have recently gained popularity in various types of genomic and epigenomic studies due to their capabilities in utilizing large-scale high-throughput bioinformatics data and achieving high accuracy in predictions and classifications. However, DNNs are often challenged by their potential to explain the predictions due to their black-box nature. In this review, we present current development in the model interpretation of DNNs, focusing on their applications in genomics and epigenomics. We first describe state-of-the-art DNN interpretation methods in representative machine learning fields. We then summarize the DNN interpretation methods in recent studies on genomics and epigenomics, focusing on current data- and computing-intensive topics such as sequence motif identification, genetic variations, gene expression, chromatin interactions and non-coding RNAs. We also present the biological discoveries that resulted from these interpretation methods. We finally discuss the advantages and limitations of current interpretation approaches in the context of genomic and epigenomic studies. Contact:xiaoman@mail.ucf.edu, haihu@cs.ucf.edu

show abstract

“…I N recent years, convolutional neural networks have attracted a lot of attention and been successfully applied to various computer vision problems, such as object detection [24], [29], [46], face recognition [8], depth estimation [16], [17], image classification [7], [50], image-to-image translation [44], [45], and crowd counting [34]. Crowd counting is an integral part of crowd analysis.…”

Section: Introductionmentioning

confidence: 99%

ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images

Sajid

Sajid²,

Wang

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

Self Cite

View full text Add to dashboard Cite

This paper proposes a novel approach for crowd counting in low to high density scenarios in static images. Current approaches cannot handle huge crowd diversity well and thus perform poorly in extreme cases, where the crowd density in different regions of an image is either too low or too high, leading to crowd underestimation or overestimation. The proposed solution is based on the observation that detecting and handling such extreme cases in a specialized way leads to better crowd estimation. Additionally, existing methods find it hard to differentiate between the actual crowd and the cluttered background regions, resulting in further count overestimation. To address these issues, we propose a simple yet effective modular approach, where an input image is first subdivided into fixedsize patches and then fed to a four-way classification module labeling each image patch as low, medium, high-dense or nocrowd. This module also provides a count for each label, which is then analyzed via a specifically devised novel decision module to decide whether the image belongs to any of the two extreme cases (very low or very high density) or a normal case. Images, specified as high-or low-density extreme or a normal case, pass through dedicated zooming or normal patch-making blocks respectively before routing to the regressor in the form of fixed-size patches for crowd estimate. Extensive experimental evaluations demonstrate that the proposed approach outperforms the state-of-the-art methods on four benchmarks under most of the evaluation criteria.

show abstract

Object Detection with Convolutional Neural Networks

Abstract: To talk about object detection, we have to mention image classification. Image classification is the task of assigning a label to an input image from a fixed set

Cited by 36 publications

References 59 publications

A comparative study on polyp classification using convolutional neural networks

A comparative study on polyp classification using convolutional neural networks

Interpretation of deep learning in genomics and epigenomics

ZoomCount: A Zooming Mechanism for Crowd Counting in Static Images

Contact Info

Product

Resources

About