The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Deep convolutional neural network based medical image classification for disease diagnosis
Open accessAbstract
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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“In recent years, deep learning (DL) methodologies, particularly Convolutional Neural Networks (CNNs), have emerged as powerful tools for automatically detecting chest diseases using medical images like CT scans and X-rays [2–4].”
From Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens (2024) · cites this paper as [5]
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“To this end, we trained several CNN models, including VGG-19, ResNet-50, ResNet-101, DenseNet169 and MobileNetV2 on both datasets. These architectures have shown promising results in similar automatic diagnostic tasks in medical imaging [4, 5].”
From Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens (2024) · cites this paper as [5]
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Cites
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Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM
Srinivasu, P. N., SivaSai, J. G., Ijaz, M. F., Bhoi, A. K., Kim, W., & Kang, J. J. (2021) · Sensors · doi:10.3390/s21082852 · Cited by 763
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Metadata
- Authors
- Yadav, S. S., & Jadhav, S. M.
- Affiliations
- Dr. Babasaheb Ambedkar Technological University (IN)
- Corresponding author
- Yadav, Samir S.
- Venue
- Journal of Big Data
- Published
- 2019-12
- Volume
- 6
- Issue
- 1
- ISSN
- 2196-1115 (ISSN-L)
- 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
- 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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