An Explainable AI-Enabled Framework for Interpreting Pulmonary Diseases from Chest Radiographs

Naz, Zubaira; Khan, Muhammad Usman Ghani; Saba, Tanzila; Khan, Azmat Ullah; Nobanee, Haitham; Bahaj, Saeed Ali

doi:10.3390/cancers15010314

Cited by 21 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MRI is impossible if the brain aneurysm clips are constructed of ferromagnetic materials. The examination is feasible if the clips are titanium alloy (Kurdi et al 2023 ; Naz et al 2023 ).…”

Section: Ai-based Systems For Cancer Diagnosismentioning

confidence: 99%

Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions

Alyami

2024

EJNMMI Rep.

View full text Add to dashboard Cite

Radiological image analysis using machine learning has been extensively applied to enhance biopsy diagnosis accuracy and assist radiologists with precise cures. With improvements in the medical industry and its technology, computer-aided diagnosis (CAD) systems have been essential in detecting early cancer signs in patients that could not be observed physically, exclusive of introducing errors. CAD is a detection system that combines artificially intelligent techniques with image processing applications thru computer vision. Several manual procedures are reported in state of the art for cancer diagnosis. Still, they are costly, time-consuming and diagnose cancer in late stages such as CT scans, radiography, and MRI scan. In this research, numerous state-of-the-art approaches on multi-organs detection using clinical practices are evaluated, such as cancer, neurological, psychiatric, cardiovascular and abdominal imaging. Additionally, numerous sound approaches are clustered together and their results are assessed and compared on benchmark datasets. Standard metrics such as accuracy, sensitivity, specificity and false-positive rate are employed to check the validity of the current models reported in the literature. Finally, existing issues are highlighted and possible directions for future work are also suggested.

show abstract

“…MRI is impossible if the brain aneurysm clips are constructed of ferromagnetic materials. The examination is feasible if the clips are titanium alloy (Kurdi et al 2023 ; Naz et al 2023 ).…”

Section: Ai-based Systems For Cancer Diagnosismentioning

confidence: 99%

Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions

Alyami

2024

EJNMMI Rep.

View full text Add to dashboard Cite

show abstract

“…Additionally, the use of the dataset requires careful consideration of ethical and privacy concerns related to patient data. Table 3 shows that the features of LUNA16dataset [38]- [40]. The diameter of the nodule in millimeters Series UID Unique identifier for the series that contains the nodule CAD probability…”

Section: Lung Nodule Analysis Datasetmentioning

confidence: 99%

Comparative analysis of explainable AI models for predicting lung cancer using diverse datasets

Makubhai,

R Pathak,

R Chandre

2024

IJ-AI

View full text Add to dashboard Cite

Lung cancer prediction is crucial for early detec-tion and treatment, and explainable AI models have gained attention for their interpretability. This study aims to compare various explainable AI models using diverse datasets for lung cancer prediction. Clinical, genomic, and imaging data from multiple sources were collected, prepro-cessed, and used to train models such as Logistic Regression, SVC-Linear, SVC-rbf, Decision Tree, Random Forest, AdaBoost Classifier, and XGBoost Classifier. Preliminary results indicate that Random Forest achieved the highest accuracy of 98.9% across multiple datasets. Evaluation metrics such as accuracy, precision, recall, and F1 score were utilized, along with interpretability techniques like feature importance rankings and rule extraction methods. The study's findings will aid in identifying effective and interpretable AI models, facilitating early detection and treatment decisions for lung cancer.

show abstract

“…In addition, this makes the approach prone to variability among dissimilar screening parameters and different CT scans. The benefit of utilizing deep learning (DL) in CAD systems is that it could implement end-to-end recognition by learning one of the important features in a trained model [13,14]. This enables the network to work effectively when there is variation, as it captures nodule features in CT scans with different parameters [15].…”

Section: Introductionmentioning

confidence: 99%

Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification

Alamgeer

Alruwais

Alshahrani

et al. 2023

Cancers

View full text Add to dashboard Cite

Lung cancer is the main cause of cancer deaths all over the world. An important reason for these deaths was late analysis and worse prediction. With the accelerated improvement of deep learning (DL) approaches, DL can be effectively and widely executed for several real-world applications in healthcare systems, like medical image interpretation and disease analysis. Medical imaging devices can be vital in primary-stage lung tumor analysis and the observation of lung tumors from the treatment. Many medical imaging modalities like computed tomography (CT), chest X-ray (CXR), molecular imaging, magnetic resonance imaging (MRI), and positron emission tomography (PET) systems are widely analyzed for lung cancer detection. This article presents a new dung beetle optimization modified deep feature fusion model for lung cancer detection and classification (DBOMDFF-LCC) technique. The presented DBOMDFF-LCC technique mainly depends upon the feature fusion and hyperparameter tuning process. To accomplish this, the DBOMDFF-LCC technique uses a feature fusion process comprising three DL models, namely residual network (ResNet), densely connected network (DenseNet), and Inception-ResNet-v2. Furthermore, the DBO approach was employed for the optimum hyperparameter selection of three DL approaches. For lung cancer detection purposes, the DBOMDFF-LCC system utilizes a long short-term memory (LSTM) approach. The simulation result analysis of the DBOMDFF-LCC technique of the medical dataset is investigated using different evaluation metrics. The extensive comparative results highlighted the betterment of the DBOMDFF-LCC technique of lung cancer classification.

show abstract

An Explainable AI-Enabled Framework for Interpreting Pulmonary Diseases from Chest Radiographs

Cited by 21 publications

References 31 publications

Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions

Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions

Comparative analysis of explainable AI models for predicting lung cancer using diverse datasets

Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification

Contact Info

Product

Resources

About