ArticleActa informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH2020
A Comparative Analysis of Data Augmentation Approaches for Magnetic Resonance Imaging (MRI) Scan Images of Brain Tumor.
Article in Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neuroimage analysis using artificial intelligence approaches: a systematic review.Medical & biological engineering & computing · 2024Pooled it
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- Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling.Journal of cancer research and clinical oncology · 2024Article
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- A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics.Journal of cancer research and clinical oncology · 2023Review
- Deep Learning Framework for Liver Segmentation fromSensors (Basel, Switzerland) · 2023Article
- Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach.Diagnostics (Basel, Switzerland) · 2021Article
- Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning.Frontiers in bioengineering and biotechnology · 2021Article
- Application of Artificial Intelligence in Diagnosis of Craniopharyngioma.Frontiers in neurology · 2021Review
- Artificial Intelligence Comparison of the Radiologist Report With Endoscopic Predictors of Successful Transforaminal Decompression for Painful Conditions of the Lumber Spine: Application of Deep Learning Algorithm Interpretation of Routine Lumbar Magnetic Resonance Imaging Scan.International journal of spine surgery · 2020Article
- Feasibility of Deep Learning Algorithms for Reporting in Routine Spine Magnetic Resonance Imaging.International journal of spine surgery · 2020Article
- Automatic Detection of Coronary Metallic Stent Struts Based on YOLOv3 and R-FCN.Computational and mathematical methods in medicine · 2020Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
introductionMachine Learning (ML) is a rapidly growing subfield of Artificial Intelligence (AI). It is used for different purposes in our daily life such as face recognition, speech recognition, text translation in different languages, weather prediction, and business prediction. In parallel, ML also plays an important role in the medical domain such as in medical imaging. ML has various algorithms that need to be trained with large volumes of data to produce a well-trained model for prediction.
aimThe aim of this study is to highlight the most suitable Data Augmentation (DA) technique(s) for medical imaging based on their results.
methodsDA refers to different approaches that are used to increase the size of datasets. In this study, eight DA approaches were used on publicly available low-grade glioma tumor datasets obtained from the Tumor Cancer Imaging Archive (TCIA) repository. The dataset included 1961 MRI brain scan images of low-grade glioma patients. You Only Look Once (YOLO) version 3 model was trained on the original dataset and the augmented datasets separately. A neural network training/testing ecosystem named as supervisely with Tesla K80 GPU was used for YOLO v3 model training on all datasets.
resultsThe results showed that the DA techniques rotate at 180o and rotate at 90o performed the best as data enhancement techniques for medical imaging.
conclusionRotation techniques are found significant to enhance the low volume of medical imaging datasets.
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