Notice of Retraction: Infrared Sensing Based Non-Invasive Initial Diagnosis of Chronic Liver Disease Using Ensemble Learning

Rehman, Mujeeb Ur; Najam, Shaheryar; Khalid, Sohail; Shafique, Arslan; Alqahtani, Fehaid; Baothman, Fatmah; Shah, Syed Yaseen; Abbasi, Qammer H.; Ahmad, Jawad

doi:10.1109/jsen.2021.3091471

Cited by 21 publications

(10 citation statements)

References 55 publications

(39 reference statements)

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“…Living with cancer can be a long-term, arduous experience that can negatively impact a person’s mental health. The side effects of cancer treatments, such as chemotherapy and radiation therapy, can negatively impact a patient’s mental and physical health via Anatomy Aware Convolutional Neural Network (ACNNs) and their extensions (Kamal et al 2022 ; Biswas et al 2021 ; Rehman 2021 ; https://data.4tu.nl/articles/dataset/Geothermal_Project_on_TU_Delft_Campus_-_DAPGEO-02_Core_CT-Scan_Data/21528819 ). Mental health conditions such as depression and anxiety can increase the risk of developing certain types of cancer or worsen the prognosis for cancer patients.…”

Section: Introductionmentioning

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RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis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

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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show abstract

“…Each time model is trained on k-1 data sets and tested on the remaining 1 group. The utilization of test data sets for validation purposes helped to prevent the risk of the model being overfitted to the training data sets, thereby eliminating any potential bias in the performance estimate [34]. In the current study, a 10-fold cross k-fold cross-validation method was used.…”

Section: Evaluation Of the Classification Modelmentioning

confidence: 99%

“…The performance is usually evaluated in terms of different statistical term/indicators. The most commonly used indicators are accuracy, specificity, sensitivity, precision, F-score, and AUC described below [34]. Where N gives the data groups, TP (true positive), TN (true negative), FP (false positive) and FN (false negative)…”

Section: Evaluation Of the Classification Modelmentioning

confidence: 99%

Raman spectroscopy assisted support vector machine: a steadfast tool for noninvasive classification of urinary glucose of diabetes mellitus

Rehan,

Khan,

Ullah

2024

Phys. Scr.

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The current study presents the application of a Raman spectroscopy-assisted Support Vector Machine (SVM) algorithm for the analysis and classification of urine samples of diabetes mellitus and healthy control subjects. Raman spectra of pathological as well as control samples were acquired in the spectral range of 600 to 1800 cm-1. Spectral differences were observed in Raman spectra of diabetic and non-diabetic urine samples based on variations in their chemical composition. Effective machine-learning techniques were utilized to highlight the spectral differences between diabetic and normal urine samples. Data classification was carried out by using SVM models with different kernel functions: radial basis function (RBF), polynomial function, linear function, and sigmoid. The algorithm classifies data set into different classes based on even subtle variations in the spectral features. The classification performance of the model was evaluated by employing a 10-fold cross-validation method. The best performance was obtained with RBF having a diagnostic accuracy, precision, sensitivity, specificity, F-score, and AUC of approximately 94 %, 100 %, 90 %, 100 %, 0.94, and 0.99 respectively.

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“…Rehman et al, 24 proposed an iris feature‐based noninvasive technique by incorporating a novel machine‐learning algorithm. The experimental setup involved data set for the models' training included 879 subjects from Pakistan, of which 453 subjects have chronic liver disease and 426 are healthy.…”

Section: Literature Reviewmentioning

confidence: 99%

Early detection of liver disorders using hybrid soft computing techniques for optimal feature selection and classification

Varchagall

Yogegowda

2022

Concurrency and Computation

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Summary Human liver disease is a sort of illness that starts in the ovaries and is particularly dangerous for women. As a consequence, aberrant cells develop that have the potential to spread to other parts of the body. Liver disease is a serious disorder that affects women's ovaries and is difficult to identify early on, which is why it is still one of the leading causes of mortality. The significance of unequivocal confirmation of intrinsic and typical components in establishing new frameworks to detect and eliminate danger is substantial. In this study, we present a hybrid soft computing approach for early diagnosis of liver problems (EDLD‐HS). For high and low level feature extraction models, we first employ an improved ant swarm optimization (IASO) approach. Then, for optimum feature selection, we develop a modified whale search optimization (MWSO) technique that combines features that may reflect both texture patterns and semantic backdrop scattered in the data. After that, we employed a hybrid swallow swarm intelligent‐deep neural network (HSSI‐DNN) classifier to determine the stage of liver illness. Finally, we used MATLAB R2014a to test our proposed EDLD‐HS method using well‐known benchmark datasets including BUPA, ILPD, and MPRLPD. The simulation findings are compared to existing state‐of‐the‐art methodologies in terms of accuracy, specificity, sensitivity, precision, recall, F1‐score, G‐mean, and area under curve (AUC). The detection accuracy of the proposed HSSI‐DNN classifier is 83.26% (BUPA), 84.26% (ILPD), and 91.23% (ILPD) (MPRLPD).

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Notice of Retraction: Infrared Sensing Based Non-Invasive Initial Diagnosis of Chronic Liver Disease Using Ensemble Learning

Cited by 21 publications

References 55 publications

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

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

Raman spectroscopy assisted support vector machine: a steadfast tool for noninvasive classification of urinary glucose of diabetes mellitus

Early detection of liver disorders using hybrid soft computing techniques for optimal feature selection and classification

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