Deep convolutional neural network based medical image classification for disease diagnosis

By Samir S. Yadav, Shivajirao Manikrao Jadhav. Journal of Big Data, 2019

884 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9eb47e857cdf1f97413de0e3993f8429d7c33503

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep learning based detection and analysis of COVID-19 on chest X-ray images (2020, methodology): “In [42], the authors CNN based algorithm for analyzing pneumonia by using a chest X-ray dataset.”
Machine-Learning-Based Disease Diagnosis: A Comprehensive Review (2021, background): “Yadav & Jadvav (2019) [30] Deep Convolutional Neural Network based medical image classification for disease diagnosis 155”
Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM (2021, background): “Deep Neural Network model-based skin disease classiﬁcation [53,54] has exhibited a notable performance in classifying skin diseases.”
Transfer learning for image classification using VGG19: Caltech-101 image data set (2021, methodology): “Yadav (2019) evaluated the performance of the CNN based model using VGG16 and inception over the traditional image classification model using ORB and SVM.”
COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches (2020, neutral): “[9] performed a classification algorithm using pneumonia data, SVM as a classification method, and InceptionV3, VGG-16 models as a deep learning approach.”