Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI

Bhandari, Mohan; Shahi, Tej Bahadur; Siku, Birat; Neupane, Arjun

doi:10.1016/j.compbiomed.2022.106156

Cited by 61 publications

(34 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the BlackBox nature of DL algorithms as well as due to growing complexities, the need for explainability is increasing rapidly, especially in image processing [ 42 , 43 , 44 ], criminal investigation [ 45 , 46 ], forensic [ 47 , 48 , 49 ], etc. Professionals from these sectors may find it easier to comprehend the DL model’s findings and apply them to swiftly and precisely assess whether a face is real or artificial.…”

Section: Methodsmentioning

confidence: 99%

Auguring Fake Face Images Using Dual Input Convolution Neural Network

et al. 2022

Self Cite

View full text Add to dashboard Cite

Deepfake technology uses auto-encoders and generative adversarial networks to replace or artificially construct fine-tuned faces, emotions, and sounds. Although there have been significant advancements in the identification of particular fake images, a reliable counterfeit face detector is still lacking, making it difficult to identify fake photos in situations with further compression, blurring, scaling, etc. Deep learning models resolve the research gap to correctly recognize phony images, whose objectionable content might encourage fraudulent activity and cause major problems. To reduce the gap and enlarge the fields of view of the network, we propose a dual input convolutional neural network (DICNN) model with ten-fold cross validation with an average training accuracy of 99.36 ±0.62, a test accuracy of 99.08 ± 0.64, and a validation accuracy of 99.30 ± 0.94. Additionally, we used ’SHapley Additive exPlanations (SHAP) ’ as explainable AI (XAI) Shapely values to explain the results and interoperability visually by imposing the model into SHAP. The proposed model holds significant importance for being accepted by forensics and security experts because of its distinctive features and considerably higher accuracy than state-of-the-art methods.

show abstract

Section: Methodsmentioning

confidence: 99%

Auguring Fake Face Images Using Dual Input Convolution Neural Network

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…DL models have made significant advances in a variety of fields including, but not limited to, deep fakes [ 22 , 23 ], satellite image analysis [ 24 ], image classification [ 25 , 26 ], the optimization of artificial neural networks [ 27 , 28 ], the processing of natural language [ 29 , 30 ], fin-tech [ 31 ], intrusion detection [ 32 ], steganography [ 33 ], and biomedical image analysis [ 14 , 34 ]. CNNs have recently surfaced as one of the most commonly used techniques for plant disease identification [ 35 , 36 ].…”

Section: Related Workmentioning

confidence: 99%

“…Although the existing deep-learning models for tomato-leaf-disease recognition achieved high accuracy on selected leaf image datasets, their interpretability and explainability are not sufficiently investigated to engender trust in using such models in practice. The eXplainable Artificial Intelligence (XAI) and DL algorithms that produce human-readable explanations for AI judgments lay the groundwork for imaging-based artificial-intelligence applications [ 13 ] in various domains, such as health informatics [ 14 ], computer vision [ 15 ], and many more.…”

Section: Introductionmentioning

confidence: 99%

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

et al. 2023

Self Cite

View full text Add to dashboard Cite

Early and accurate tomato disease detection using easily available leaf photos is essential for farmers and stakeholders as it help reduce yield loss due to possible disease epidemics. This paper aims to visually identify nine different infectious diseases (bacterial spot, early blight, Septoria leaf spot, late blight, leaf mold, two-spotted spider mite, mosaic virus, target spot, and yellow leaf curl virus) in tomato leaves in addition to healthy leaves. We implemented EfficientNetB5 with a tomato leaf disease (TLD) dataset without any segmentation, and the model achieved an average training accuracy of 99.84% ± 0.10%, average validation accuracy of 98.28% ± 0.20%, and average test accuracy of 99.07% ± 0.38% over 10 cross folds.The use of gradient-weighted class activation mapping (GradCAM) and local interpretable model-agnostic explanations are proposed to provide model interpretability, which is essential to predictive performance, helpful in building trust, and required for integration into agricultural practice.

show abstract

“…In [ 21 ] , the author use ensemble of ML for logic driving of anthropometric measurements influencing body mass index (BMI). Additional evidence for the implementations of several XAI models is mentioned in [ 22 ] . The paper shows how integrating XAI models helps to increase the persuasive and coherence levels in the decision making of clinicians and medical professionals teams.…”

Section: Xai For Healthcarementioning

confidence: 99%

Interpretable AI for bio-medical applications

Sathyan¹,

Weinberg²,

Cohen³

2022

Complex Eng Syst

View full text Add to dashboard Cite

This paper presents the use of two popular explainability tools called Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to explain the predictions made by a trained deep neural network. The deep neural network used in this work is trained on the UCI Breast Cancer Wisconsin dataset. The neural network is used to classify the masses found in patients as benign or malignant based on 30 features that describe the mass. LIME and SHAP are then used to explain the individual predictions made by the trained neural network model. The explanations provide further insights into the relationship between the input features and the predictions. SHAP methodology additionally provides a more holistic view of the effect of the inputs on the output predictions. The results also present the commonalities between the insights gained using LIME and SHAP. Although this paper focuses on the use of deep neural networks trained on UCI Breast Cancer Wisconsin dataset, the methodology can be applied to other neural networks and architectures trained on other applications. The deep neural network trained in this work provides a high level of accuracy. Analyzing the model using LIME and SHAP adds the much desired benefit of providing explanations for the recommendations made by the trained model.

show abstract

Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI

Cited by 61 publications

References 45 publications

Auguring Fake Face Images Using Dual Input Convolution Neural Network

Auguring Fake Face Images Using Dual Input Convolution Neural Network

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

Interpretable AI for bio-medical applications

Contact Info

Product

Resources

About