ArticleJournal of imaging2023
Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification.
Article in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
- CMT-BUSNet: Adaptive Fusion-Based Triple-Branch Hybrid Architecture for Explainable Breast Ultrasound Tumor Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- Opening the black box: explainable AI for automated bioturbation analysis in cores and outcrops.Scientific reports · 2026Article
- Demystifying Deep Learning Decisions in Leukemia Diagnostics Using Explainable AI.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning for Three-Class Classification of ground-glass nodules on non-enhanced chest CT: A multicenter comparative study of CNN architectures.European journal of radiology open · 2025Article
- From texture analysis to artificial intelligence: global research landscape and evolutionary trajectory of radiomics in hepatocellular carcinoma.Discover oncology · 2025Article
- Advanced skin cancer prediction with medical image data using MobileNetV2 deep learning and optimized techniques.Scientific reports · 2025Article
- Breast Lesion Detection Using Weakly Dependent Customized Features and Machine Learning Models with Explainable Artificial Intelligence.Journal of imaging · 2025Article
- Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data.Journal of imaging · 2025Article
- SqueezeViX-Net with SOAE: A Prevailing Deep Learning Framework for Accurate Pneumonia Classification using X-Ray and CT Imaging Modalities.Current medical imaging · 2025Article
- A literature review of artificial intelligence (AI) for medical image segmentation: from AI and explainable AI to trustworthy AI.Quantitative imaging in medicine and surgery · 2024Review
- Unveiling the black box: A systematic review of Explainable Artificial Intelligence in medical image analysis.Computational and structural biotechnology journal · 2024Review
- Hybrid Feature Mammogram Analysis: Detecting and Localizing Microcalcifications Combining Gabor, Prewitt, GLCM Features, and Top Hat Filtering Enhanced with CNN Architecture.Diagnostics (Basel, Switzerland) · 2024Article
- Machine Learning of Urine Cytology Highlights Increased Neutrophil Count in Muscle-Invasive Urothelial Carcinoma.Journal of cytologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, deep learning has gained significant attention as a noteworthy division of artificial intelligence (AI) due to its high accuracy and versatile applications. However, one of the major challenges of AI is the need for more interpretability, commonly referred to as the black-box problem. In this study, we introduce an explainable AI model for medical image classification to enhance the interpretability of the decision-making process. Our approach is based on segmenting the images to provide a better understanding of how the AI model arrives at its results. We evaluated our model on five datasets, including the COVID-19 and Pneumonia Chest X-ray dataset, Chest X-ray (COVID-19 and Pneumonia), COVID-19 Image Dataset (COVID-19, Viral Pneumonia, Normal), and COVID-19 Radiography Database. We achieved testing and validation accuracy of 90.6% on a relatively small dataset of 6432 images. Our proposed model improved accuracy and reduced time complexity, making it more practical for medical diagnosis. Our approach offers a more interpretable and transparent AI model that can enhance the accuracy and efficiency of medical diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.