Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm

Tandel, Gopal S.; Balestrieri, Antonella; Jujaray, Tanay; Khanna, Narinder N.; Saba, Luca; Suri, Jasjit S.

doi:10.1016/j.compbiomed.2020.103804

Cited by 173 publications

(110 citation statements)

References 56 publications

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“…Weakness : Due to lack of non-COVID pneumonia data sets, the current models could not be tried. We intend to extend this to multiclass paradigms in future research [ 37 ]. Due to the limitation on the data sets regarding the “censorship” and “survival”, it was not possible to compute the survival analysis such as hazard curves and survival curves.…”

Section: Discussionmentioning

confidence: 99%

“…Superior lung CAD models can be designed to improve scientific validation [ 12 , 45 ]. Since AI has fast developed and more transfer learning approaches have been developed, one can try extending the TL models using the pre-trained weights [ 37 ]. While six AI models were tried on a single set of data, multi-centre study could be conducted using the same models to avoid any bias.…”

Section: Discussionmentioning

confidence: 99%

“…Cohen Kappa and F1 score are measure of AI methods performance metrics calculated based on true positive, false positive and true negative and false negative values. F1 score [ 37 ] can be calculated using the formula: …”

Section: Performance Evaluation Of Ai Models and Its Clinical Validatmentioning

confidence: 99%

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Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs

et al. 2021

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Background COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. Methodology Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation. Results Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 ( p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models. Conclusions We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference. Supplementary Information The online version contains supplementary material available at 10.1007/s11548-021-02317-0.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs

et al. 2021

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“…The study can be extended for larger and diversified cohorts. We intend to bring viral pneumonia cases over time and for better multi-class ML models [87,88]. More automated CT lung segmentation tools can be tried [11,89,90] as COVID data evolves and readily get available.…”

Section: Extensionsmentioning

confidence: 99%

A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort

et al. 2021

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Computer Tomography (CT) is currently being adapted for visualization of COVID-19 lung damage. Manual classification and characterization of COVID-19 may be biased depending on the expert's opinion. Artificial Intelligence has recently penetrated COVID-19, especially deep learning paradigms. There are nine kinds of classification systems in this study, namely one deep learning-based CNN, five kinds of transfer learning (TL) systems namely VGG16, DenseNet121, DenseNet169, DenseNet201 and MobileNet, three kinds of machine-learning (ML) systems, namely artificial neural network (ANN), decision tree (DT), and random forest (RF) that have been designed for classification of COVID-19 segmented CT lung against Controls. Three kinds of characterization systems were developed namely (a) Block imaging for COVID-19 severity index (CSI); (b) Bispectrum analysis; and (c) Block Entropy. A cohort of Italian patients with 30 controls (990 slices) and 30 COVID-19 patients (705 slices) was used to test the performance of three types of classifiers. Using K10 protocol (90% training and 10% testing), the best accuracy and AUC was for DCNN and RF pairs were 99.41 ± 5.12%, 0.991 (p < 0.0001), and 99.41 ± 0.62%, 0.988 (p < 0.0001), respectively, followed by other ML and TL classifiers. We show that diagnostics odds ratio (DOR) was higher for DL compared to ML, and both, Bispecturm and Block Entropy shows higher values for COVID-19 patients. CSI shows an association with Ground Glass Opacities (0.9146, p < 0.0001). Our hypothesis holds true that deep learning shows superior performance compared to machine learning models. Block imaging is a powerful novel approach for pinpointing COVID-19 severity and is clinically validated.

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“…Deep learning (DL), on the contrary, is a ‘class’ of AI that has recently revolutionised image classification framework [33, 34 ]. Even more recently, the paradigm of transfer learning (TL) brings a reduction in training time which is deep rooted in conventional DL frameworks [35 ]. Since the crux of the classification process seems to be partially resolved both in terms of feature extraction and prevention of training time, we can hypothesise that our WD computer‐aided design system consisting of (a) MobileNet and (b) Visual Geometric Group‐19 (VGG‐19) will be more accurate compared to existing ML systems.…”

Section: Introductionmentioning

confidence: 99%

Brain MRI‐based Wilson disease tissue classification: an optimised deep transfer learning approach

et al. 2020

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Wilson's disease (WD) is caused by the excessive accumulation of copper in the brain and liver, leading to death if not diagnosed early. WD shows its prevalence as white matter hyperintensity (WMH) in MRI scans. It is challenging and tedious to classify WD against controls when comparing visually, primarily due to subtle differences in WMH. This Letter presents a computer‐aided design‐based automated classification strategy that uses optimised transfer learning (TL) utilising two novel paradigms known as (i) MobileNet and (ii) the Visual Geometric Group‐19 (VGG‐19). Further, the authors benchmark TL systems against a machine learning (ML) paradigm. Using four‐fold augmentation, VGG‐19 is superior to MobileNet demonstrating accuracy and area under the curve (AUC) pairs as 95.46 ± 7.70 %, 0.932 (p < 0.0001 ) and 86.87 ± 2.23 %, 0.871 (p < 0.0001 ), respectively. Further, MobileNet and VGG‐19 showed an improvement of 3.4 and 13.5%, respectively, when benchmarked against the ML‐based soft classifier – Random Forest.

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Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm

Cited by 173 publications

References 56 publications

Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs

Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs

A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort

Brain MRI‐based Wilson disease tissue classification: an optimised deep transfer learning approach

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