Content-based medical image retrieval using deep learning-based features and hybrid meta-heuristic optimization

Shetty, Rani; Bhat, Vandana S.; Pujari, Jagadeesh

doi:10.1016/j.bspc.2024.106069

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Cited by 7 publications

References 32 publications

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Content-Based CT Image Retrieval for Emphysema Using Texture and Shape Features

Prakash,

Singh

2024

SN COMPUT. SCI.

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Content-Based CT Image Retrieval for Emphysema Using Texture and Shape Features

Prakash,

Singh

2024

SN COMPUT. SCI.

View full text Add to dashboard Cite

An adaptation of hybrid binary optimization algorithms for medical image feature selection in neural network for classification of breast cancer

Oyelade,

Aminu,

Wang

et al. 2025

Neurocomputing

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Convolutional neural network for oral cancer detection combined with improved tunicate swarm algorithm to detect oral cancer

Wei,

Chanjuan,

et al. 2024

Sci Rep

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Early Diagnosis of oral cancer is very important and can save you from some oral malignancies. However, while this approach aids in the rapid healing of patients and the preservation of their lives, there are several causes for poor and wrong diagnosis of oral cancer. In recent years, the use of computer-aided design diagnosis tools as an auxiliary tool alongside clinicians has greatly benefited in more accurate identification of this malignancy. The current study proposes a new approach for identifying oral cancer patients based on image processing and deep learning. The current study employs a recently integrated model of an improved tunicate swarm algorithm to produce an efficient tool for improving a convolutional neural network and delivering an accurate cancer diagnostic system. The approach is then implemented on the oral cancer pictures dataset. The approach is then validated by comparing it to other published papers using various measurement markers. The proposed model achieved an accuracy of 98.70% and a recall of 93.71% in detecting oral cancerous lesions from photographic images. The model also achieved an F1-score of 90.08% and a precision of 96.42%. The final results demonstrate that the offered approach can produce more exact results and can be used in conjunction with clinicians to help in diagnosing oral cancer.

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Content-based medical image retrieval using deep learning-based features and hybrid meta-heuristic optimization

Cited by 7 publications

References 32 publications

Content-Based CT Image Retrieval for Emphysema Using Texture and Shape Features

Content-Based CT Image Retrieval for Emphysema Using Texture and Shape Features

An adaptation of hybrid binary optimization algorithms for medical image feature selection in neural network for classification of breast cancer

Convolutional neural network for oral cancer detection combined with improved tunicate swarm algorithm to detect oral cancer

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