Mathematical Modeling, In-Human Evaluation and Analysis of Volume Kinetics and Kidney Function After Burn Injury and Resuscitation

Arabidarrehdor, Ghazal; Tivay, Ali; Meador, C. Lawrence; Kramer, George C.; Hahn, Jin‐Oh; Salinas, José

doi:10.1109/tbme.2021.3094515

Cited by 7 publications

(1 citation statement)

References 60 publications

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“…Despite this, CNN is still the news channel that attracts the most viewers. In an attempt to challenge CNN’s preeminent position in this sector, researchers are focusing their efforts on enhancing the CNN-Transformer hybrid models’ capacity to correctly segment medical images (Cui 2021 ; ArabiDarrehDor et al 2022 ; Antunes 2022 ; Wagner et al 2022 ). Researchers (Hussain et al 2021 ; AlGhamdi et al 2020 ; Panayides 2020 ) created efficient encoders for segmenting two-dimensional medical images by combining a transformer with a convolutional neural network encoder.…”

Section: Review Of Existing Multiorgan Disease Detection Techniquesmentioning

confidence: 99%

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

Ajani¹,

Mulla²,

Limkar³

et al. 2023

Soft Comput

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Progressive organ-level disorders in the human body are often correlated with diseases in other body parts. For instance, liver diseases can be linked with heart issues, while cancers can be linked with brain diseases (or psychological conditions). Defining such correlations is a complex task, and existing deep learning models that perform this task either showcase lower accuracy or are non-comprehensive when applied to real-time scenarios. To overcome these issues, this text proposes design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. The proposed model initially collects temporal and spatial data scans for different body parts and uses a multidomain feature extraction engine to convert these scans into vector sets. These vectors are processed by a Bacterial Foraging Optimizer (BFO), which assists in identification of highly variant feature sets, which are individually classified into different disease categories. A fusion of Inception Net, XCeption Net, and GoogLeNet Models is used to perform these classifications. The classified categories are linked with other disease types via temporal analysis of blood reports. The temporal analysis engine uses Modified Analytical Hierarchical Processing (MAHP) Model for calculating inter-organ disease dependency probabilities. Based on these probabilities, the model is able to generate a patient-level correlation map, which can be used by clinical experts to suggest remedial treatments, due to which the model was able to identify correlations between brain disorders and kidneys, heart diseases and lungs, heart diseases and liver, brain diseases and different types of cancers with high efficiency when evaluated under clinical scenarios. When validated on MITBIH, DEAP, CT Kidney, RIDER, and PLCO data samples, it was observed that the proposed model was capable of improving accuracy of correlation by 8.5%, while improving precision and recall by 3.2% when compared with existing correlation models under similar clinical scenarios.

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