ETL for disease indicators using brute force rule-based NLP algorithm and metadata exploration

doi:10.19101/ijatee.2021.875069

Cited by 1 publication

(1 citation statement)

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“…Machine Learning and deep learning have already been utilized for various healthcare tasks like in (Altaf et al, 2021) (Ashraf et al, 2021) (Fayaz et al, 2022) (Riyaz et al, 2022) (Fayaz et al, 2021) (Altaf et al, 2022) (Rehman et al, 2022). In general, several deep learning models have been developed for the segmentation of different medical image modalities which have been discussed in our review (Rehman et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Attention Res-UNet

Rehman

Butt

Zaman

2022

International Journal of Decision Support System Technology

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During a dermoscopy examination, accurate and automatic skin lesion detection and segmentation can assist medical experts in resecting problematic areas and decrease the risk of deaths due to skin cancer. In order to develop fully automated deep learning model for skin lesion segmentation, the authors design a model Attention Res-UNet by incorporating residual connections, squeeze and excite units, atrous spatial pyramid pooling, and attention gates in basic UNet architecture. This model uses focal tversky loss function to achieve better trade off among recall and precision when training on smaller size lesions while improving the overall outcome of the proposed model. The results of experiments have demonstrated that this design, when evaluated on publicly available ISIC 2018 skin lesion segmentation dataset, outperforms the existing standard methods with a Dice score of 89.14% and IoU of 81.16%; and achieves better trade off among precision and recall. The authors have also performed statistical test of this model with other standard methods and evaluated that this model is statistically significant.

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