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DenseHillNet: a lightweight CNN for accurate classification of natural images

PeerJ Computer Science, ISSN: 2376-5992, Vol: 10, Page: e1995
2024
  • 4
    Citations
  • 0
    Usage
  • 3
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    4
  • Captures
    3
  • Mentions
    1
    • News Mentions
      1
      • News
        1

Most Recent News

Researchers from Gomal University Describe Findings in Computer Science (DenseHillNet: a lightweight CNN for accurate classification of natural images)

2024 MAY 02 (NewsRx) -- By a News Reporter-Staff News Editor at Computer News Today -- Researchers detail new data in computer science. According to

Article Description

The detection of natural images, such as glaciers and mountains, holds practical applications in transportation automation and outdoor activities. Convolutional neural networks (CNNs) have been widely employed for image recognition and classification tasks. While previous studies have focused on fruits, land sliding, and medical images, there is a need for further research on the detection of natural images, particularly glaciers and mountains. To address the limitations of traditional CNNs, such as vanishing gradients and the need for many layers, the proposed work introduces a novel model called DenseHillNet. The model utilizes a DenseHillNet architecture, a type of CNN with densely connected layers, to accurately classify images as glaciers or mountains. The model contributes to the development of automation technologies in transportation and outdoor activities. The dataset used in this study comprises 3,096 images of each of the ‘‘glacier’’ and ‘‘mountain’’ categories. Rigorous methodology was employed for dataset preparation and model training, ensuring the validity of the results. A comparison with a previous work revealed that the proposed DenseHillNet model, trained on both glacier and mountain images, achieved higher accuracy (86%) compared to a CNN model that only utilized glacier images (72%). Researchers and graduate students are the audience of our article.

Bibliographic Details

Saqib, Sheikh Muhammad; Zubair Asghar, Muhammad; Iqbal, Muhammad; Al-Rasheed, Amal; Amir Khan, Muhammad; Ghadi, Yazeed; Mazhar, Tehseen

PeerJ

Computer Science

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