ArticleJournal of clinical medicine2019
Effective Diagnosis and Treatment through Content-Based Medical Image Retrieval (CBMIR) by Using Artificial Intelligence.
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Content-based retrieval of fundus images and diabetic retinopathy detection using variants of local texture features.Radiological physics and technology · 2026Article
- Enhanced content-based image retrieval via hybrid color, texture, and deep learning features.Scientific reports · 2026Article
- Content-based X-ray image retrieval using fusion of local neighboring patterns and deep features for lung disease detection.Radiological physics and technology · 2025Article
- Towards Interactive and Interpretable Image Retrieval-Based Diagnosis: Enhancing Brain Tumor Classification with LLM Explanations and Latent Structure Preservation.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2024Article
- Deep Bayesian Quantization for Supervised Neuroimage Search.Machine learning in medical imaging. MLMI (Workshop) · 2023Article
- Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images.Sensors (Basel, Switzerland) · 2023Article
- RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion.Journal of digital imaging · 2023Article
- New Real-Time High-Density Impulsive Noise Removal Method Applied to Medical Images.Diagnostics (Basel, Switzerland) · 2023Article
- Content-Based Medical Image Retrieval System for Skin Melanoma Diagnosis Based on Optimized Pair-Wise Comparison Approach.Journal of digital imaging · 2023Article
- Multi-modal medical image classification using deep residual network and genetic algorithm.PloS one · 2023Article
- Automated Diagnosis of Optical Coherence Tomography Angiography (OCTA) Based on Machine Learning Techniques.Sensors (Basel, Switzerland) · 2022Article
- A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model.Computational intelligence and neuroscience · 2022Article
- Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model.Journal of healthcare engineering · 2022Article
- Artificial Intelligence-Based Recognition of Different Types of Shoulder Implants in X-ray Scans Based on Dense Residual Ensemble-Network for Personalized Medicine.Journal of personalized medicine · 2021Article
- Searching for pneumothorax in x-ray images using autoencoded deep features.Scientific reports · 2021Article
- Deep Bayesian Hashing With Center Prior for Multi-Modal Neuroimage Retrieval.IEEE transactions on medical imaging · 2021Article
- Deep Hybrid Convolutional Neural Network for Segmentation of Melanoma Skin Lesion.Computational intelligence and neuroscience · 2021Article
- Application Value of a Deep Convolutional Neural Network Model for Cytological Assessment of Thyroid Nodules.Journal of healthcare engineering · 2021Article
- Artificial Intelligence Tools for Refining Lung Cancer Screening.Journal of clinical medicine · 2020Review
- Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa.Sensors (Basel, Switzerland) · 2020Article
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4 authors.
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Abstract
Medical-image-based diagnosis is a tedious task' and small lesions in various medical images can be overlooked by medical experts due to the limited attention span of the human visual system, which can adversely affect medical treatment. However, this problem can be resolved by exploring similar cases in the previous medical database through an efficient content-based medical image retrieval (CBMIR) system. In the past few years, heterogeneous medical imaging databases have been growing rapidly with the advent of different types of medical imaging modalities. Recently, a medical doctor usually refers to various types of imaging modalities all together such as computed tomography (CT), magnetic resonance imaging (MRI), X-ray, and ultrasound, etc of various organs in order for the diagnosis and treatment of specific disease. Accurate classification and retrieval of multimodal medical imaging data is the key challenge for the CBMIR system. Most previous attempts use handcrafted features for medical image classification and retrieval, which show low performance for a massive collection of multimodal databases. Although there are a few previous studies on the use of deep features for classification, the number of classes is very small. To solve this problem, we propose the classification-based retrieval system of the multimodal medical images from various types of imaging modalities by using the technique of artificial intelligence, named as an enhanced residual network (ResNet). Experimental results with 12 databases including 50 classes demonstrate that the accuracy and F1.score by our method are respectively 81.51% and 82.42% which are higher than those by the previous method of CBMIR (the accuracy of 69.71% and F1.score of 69.63%).
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