Deep learning approach for disease detection in lumbosacral spine radiographs using ConvNet

Vieira, Pablo; Vogado, Luis H. S.; Lopes, Lucas Araújo; Ricardo, Ricardo; Neto, Pedro de Alcântara dos Santos; Mathew, Mano Joseph; Magalhães, Deborah; Silva, Romuere

doi:10.1080/21681163.2023.2245922

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2024

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Cited by 2 publications

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A DCNN with Jellyfish Questoptimization Algorithm in Order to Find Fetus Images

Aravinda,

Manjunatha,

Kavin

et al. 2024

2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)

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A DCNN with Jellyfish Questoptimization Algorithm in Order to Find Fetus Images

Aravinda,

Manjunatha,

Kavin

et al. 2024

2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)

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The Automated Generation of Medical Reports from Polydactyly X-ray Images Using CNNs and Transformers

Vieira,

Mathew,

Santos Neto

et al. 2024

Applied Sciences

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Pododactyl radiography is a non-invasive procedure that enables the detection of foot pathologies, as it provides detailed images of structures such as the metatarsus and phalanges, among others. This examination holds potential for employment in CAD systems. Our proposed methodology employs generative artificial intelligence to analyze pododactyl radiographs and generate automatic medical reports. We used a dataset comprising 16,710 exams, including images and medical reports on pododactylys. We implemented preprocessing of the images and text, as well as data augmentation techniques to improve the representativeness of the dataset. The proposed CAD system integrates pre-trained CNNs for feature extraction from the images and Transformers for report interpretation and generation. Our objective is to provide reports describing pododactyl pathologies, such as plantar fasciitis, bunions, heel spurs, flat feet, and lesions, among others, offering a second opinion to the specialist. The results are promising, with BLEU scores (1 to 4) of 0.612, 0.552, 0.507, and 0.470, respectively, a METEOR score of 0.471, and a ROUGE-L score of 0.633, demonstrating the model’s ability to generate reports with qualities close to those produced by specialists. We demonstrate that generative AI trained with pododactyl radiographs has the potential to assist in diagnoses from these examinations.

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Deep learning approach for disease detection in lumbosacral spine radiographs using ConvNet

Cited by 2 publications

References 38 publications

A DCNN with Jellyfish Questoptimization Algorithm in Order to Find Fetus Images

A DCNN with Jellyfish Questoptimization Algorithm in Order to Find Fetus Images

The Automated Generation of Medical Reports from Polydactyly X-ray Images Using CNNs and Transformers

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