# The use of deep learning in image segmentation, classification and detection

arXiv: https://arxiv.org/abs/1605.09612 · HTML: https://mlsanity.com/paper/1605.09612/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2016.

## Built on by (most cited first, 2 of 2)

- [Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans](https://mlsanity.com/paper/2101.07612/) (2021)
- [Deep learning assisted high resolution microscopy image processing for phase segmentation in functional composite materials](https://mlsanity.com/paper/2410.01928/) (2024)

## Abstract (arXiv)

Recent years have shown that deep learned neural networks are a valuable tool in the field of computer vision. This paper addresses the use of two different kinds of network architectures, namely LeNet and Network in Network (NiN). They will be compared in terms of both performance and computational efficiency by addressing the classification and detection problems. In this paper, multiple databases will be used to test the networks. One of them contains images depicting burn wounds from pediatric cases, another one contains an extensive number of art images and other facial databases were used for facial keypoints detection.