On Evaluating CNN Representations for Low Resource Medical Image Classification

Agrawal, Taruna; Gupta, Rahul; Narayanan, Shrikanth

doi:10.1109/icassp.2019.8682397

Cited by 21 publications

(11 citation statements)

References 19 publications

(20 reference statements)

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“…In this subsection, an overview of CNN-based methods for image classification will be presented. The CNNs used in this research are, in fact, standard CNN architectures widely used for solving various computer vision and image recognition problems [ 48 ]. Such an algorithm, alongside its variations, is widely used for various tasks of medical image recognition [ 49 ].…”

Section: Description Of Used Convolutional Neural Networkmentioning

confidence: 99%

Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

Lorencin

Šegota

Anđelić

et al. 2021

JPM

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COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.

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Section: Description Of Used Convolutional Neural Networkmentioning

confidence: 99%

Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

Lorencin

Šegota

Anđelić

et al. 2021

JPM

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show abstract

“…15 Convolutional neural network architecture is a deep learning architecture that automatically extracts and classifies images from images. 16 Reference 17 proposed a hybrid model called fused perceptual hash-based CNN to reduce the classifying time of liver CT images and maintain performance. Reference 18 used a transfer learning strategy to deal with the medical image unbalance problem.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, interest in early‐stage feature extraction methods has begun to decline with the introduction of CNN and other automated feature extraction techniques 15 . Convolutional neural network architecture is a deep learning architecture that automatically extracts and classifies images from images 16 . Reference 17 proposed a hybrid model called fused perceptual hash‐based CNN to reduce the classifying time of liver CT images and maintain performance.…”

Section: Introductionmentioning

confidence: 99%

Classification of Coronavirus (COVID‐19) from X‐ray and CT images using shrunken features

Öztürk

Özkaya

Barstuğan

2020

Int J Imaging Syst Tech

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Necessary screenings must be performed to control the spread of the COVID-19 in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the suspicious test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions for COVID-19. The information obtained by using X-ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it is aimed to develop a machine learning method for the detection of viral epidemics by analyzing X-ray and CT images. In this study, images belonging to six situations, including coronavirus images, are classified using a two-stage data enhancement approach. Since the number of images in the dataset is deficient and unbalanced, a shallow image augmentation approach was used in the first phase. It is more convenient to analyze these images with hand-crafted feature extraction methods because the dataset newly created is still insufficient to train a deep architecture. Therefore, the Synthetic minority over-sampling technique algorithm is the second data enhancement step of this study. Finally, the feature vector is reduced in size by using a stacked auto-encoder and principal component analysis methods to remove interconnected features in the feature vector. According to the obtained results, it is seen that the proposed method has leveraging performance, especially to make the diagnosis of COVID-19 in a short time and effectively. Also, it is thought to be a source of inspiration for future studies for deficient and unbalanced datasets.

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“…In addition, interest in hand-crafted methods has begun to decline with the introduction of CNN and other automated feature extraction techniques. Convolutional neural network architecture is a deep learning architecture that automatically extracts and classifies images from images [15]. Özyurt et al [16] proposed a hybrid model called fused perceptual hash-based CNN to reduce the classifying time of liver CT images and maintain performance.…”

Section: Introductionmentioning

confidence: 99%

Classification of Coronavirus Images using Shrunken Features

Öztürk

Özkaya

Barstuğan

2020

Preprint

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Necessary screenings must be performed to control the spread of the Corona Virus in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the wrong test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions with COVID-19. The information obtained by using X-ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it was aimed to develop a machine learning method for the detection of viral epidemics by analyzing X-ray images. In this study, images belonging to 6 situations, including coronavirus images, are classified. Since the number of images in the dataset is deficient and unbalanced, it is more convenient to analyze these images with hand-crafted feature extraction methods. For this purpose, firstly, all the images in the dataset are extracted with the help of four feature extraction algorithms. These extracted features are combined in raw form. The unbalanced data problem is eliminated by producing feature vectors with the SMOTE algorithm. Finally, the feature vector is reduced in size by using a stacked auto-encoder and principal component analysis to remove interconnected features in the feature vector. According to the obtained results, it is seen that the proposed method has leveraging performance, especially in order to make the diagnosis of COVID-19 in a short time and effectively.

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On Evaluating CNN Representations for Low Resource Medical Image Classification

Cited by 21 publications

References 19 publications

Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

Classification of Coronavirus (COVID‐19) from X‐ray and CT images using shrunken features

Classification of Coronavirus Images using Shrunken Features

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