Cited by 1,069COVID-19 diagnosis using AIAdvanced Neural Network ApplicationsRadiomics and Machine Learning in Medical Imaging

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Deep convolutional neural network based medical image classification for disease diagnosis

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Yadav, Samir S., Jadhav, Shivajirao M.·2019·Journal of Big Data

DOI 10.1186/s40537-019-0276-2

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Abstract

Abstract Medical image classification plays an essential role in clinical treatment and teaching tasks. However, the traditional method has reached its ceiling on performance. Moreover, by using them, much time and effort need to be spent on extracting and selecting classification features. The deep neural network is an emerging machine learning method that has proven its potential for different classification tasks. Notably, the convolutional neural network dominates with the best results on varying image classification tasks. However, medical image datasets are hard to collect because it needs a lot of professional expertise to label them. Therefore, this paper researches how to apply the convolutional neural network (CNN) based algorithm on a chest X-ray dataset to classify pneumonia. Three techniques are evaluated through experiments. These are linear support vector machine classifier with local rotation and orientation free features, transfer learning on two convolutional neural network models: Visual Geometry Group i.e., VGG16 and InceptionV3, and a capsule network training from scratch. Data augmentation is a data preprocessing method applied to all three methods. The results of the experiments show that data augmentation generally is an effective way for all three algorithms to improve performance. Also, Transfer learning is a more useful classification method on a small dataset compared to a support vector machine with oriented fast and rotated binary (ORB) robust independent elementary features and capsule network. In transfer learning, retraining specific features on a new target dataset is essential to improve performance. And, the second important factor is a proper network complexity that matches the scale of the dataset.

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Authors: Yadav, S. S., & Jadhav, S. M.
Affiliations: Dr. Babasaheb Ambedkar Technological University (IN)
Corresponding author: Yadav, Samir S.
ORCID: Yadav, Samir S.
Jadhav, Shivajirao M.
Venue: Journal of Big Data
Publisher: Springer Science and Business Media LLC
Published: 2019-12
Volume: 6
Issue: 1
ISSN: 2196-1115 (ISSN-L)
DOI: 10.1186/s40537-019-0276-2
OpenAlex: W2995942064
Cited by: 1,069
References: 30
Citations by year: 2026: 57 · 2025: 139 · 2024: 234 · 2023: 215 · 2022: 197 · 2021: 166 · 2020: 61
FWCI: 69.98
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor, 16 d)
Locations: Journal Of Big Data (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: medical image classification, pneumonia detection, chest radiography, convolutional neural networks, transfer learning, VGG16, InceptionV3, capsule networks, support vector machine, ORB features, data augmentation, small medical image datasets

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Deep convolutional neural network based medical image classification for disease diagnosis

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