Evidence map›Paper›PMID 39170344›Full record

ArticleHeliyon2024

Enhanced deep learning models for automatic fish species identification in underwater imagery.

Siri D, Gopikrishna Vellaturi, Shaik Hussain Shaik Ibrahim, Srikanth Molugu, Venkata Subbaiah Desanamukula, Raviteja Kocherla, Ramesh Vatambeti

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In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Siri DDepartment of CSE, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India.
Gopikrishna VellaturiDepartment of Information Technology, MLR Institute of Technology, Hyderabad, India.
Shaik Hussain Shaik IbrahimDepartment of Computer Science and Engineering, Malla Reddy University, Hyderabad, 500043, India.
Srikanth MoluguDepartment of Computer Science and Engineering, Malla Reddy University, Hyderabad, 500043, India.
Venkata Subbaiah DesanamukulaDepartment of Computer Science and Engineering, Lakireddy Bali Reddy College of Engineering, India.
Raviteja KocherlaDepartment of Computer Science and Engineering, Malla Reddy University, Hyderabad, 500043, India.
Ramesh VatambetiSchool of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Underwater cameras are crucial in marine ecology, but their data management needs automatic species identification. This study proposes a two-stage deep learning approach. First, the Unsharp Mask Filter (UMF) preprocesses images. Then, an enhanced region-based fully convolutional network (R-FCN) detects fish using two-order integrals for position-sensitive score maps and precise region of interest (PS-Pr-RoI) pooling for accuracy. The second stage integrates ShuffleNetV2 with the Squeeze and Excitation (SE) module, forming the Improved ShuffleNetV2 model, enhancing classification focus. Hyperparameters are optimized with the Enhanced Northern Goshawk Optimization Algorithm (ENGO). The improved R-FCN model achieves 99.94 % accuracy, 99.58 % precision and recall, and a 99.27 % F-measure on the Fish4knowledge dataset. Similarly, the ENGO-based ShuffleNetV2 model, evaluated on the same dataset, shows 99.93 % accuracy, 99.19 % precision, 98.29 % recall, and a 98.71 % F-measure, highlighting its superior classification accuracy.

Indexed as

Northern goshawk optimizationRegion-based fully convolutional networkShuffleNetSqueeze and excitationUnsharp mask filter

Identifiers

PMID39170344
PMCPMC11336429

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.