Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection

Sitaula, Chiranjibi; Shahi, Tej Bahadur; Aryal, Sunil; Marzbanrad, Faezeh

doi:10.1038/s41598-021-03287-8

Cited by 35 publications

(31 citation statements)

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“…The transfer learning and fine-tuning of MobileNetV1 [34], InceptionV3 [33] and other CNNs were implemented in [14] for fruit classification. The VGG-16 [32] is still popular CNN model for the feature extraction and has been used in various domains ranging for medical image analysis [54] to fruit classification [39]. Among these five models, 'NASNetMobile' and 'MobileNetV1' are lightweight models while the rest are considered as the large and deep convolutional neural networks (CNNs).…”

Section: Comparison With Pre-trained DL Modelsmentioning

confidence: 99%

Fruit classification using attention-based MobileNetV2 for industrial applications

et al. 2022

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Recent deep learning methods for fruits classification resulted in promising performance. However, these methods are with heavy-weight architectures in nature, and hence require a higher storage and expensive training operations due to feeding a large number of training parameters. There is a necessity to explore lightweight deep learning models without compromising the classification accuracy. In this paper, we propose a lightweight deep learning model using the pre-trained MobileNetV2 model and attention module. First, the convolution features are extracted to capture the high-level object-based information. Second, an attention module is used to capture the interesting semantic information. The convolution and attention modules are then combined together to fuse both the high-level object-based information and the interesting semantic information, which is followed by the fully connected layers and the softmax layer. Evaluation of our proposed method, which leverages transfer learning approach, on three public fruit-related benchmark datasets shows that our proposed method outperforms the four latest deep learning methods with a smaller number of trainable parameters and a superior classification accuracy. Our model has a great potential to be adopted by industries closely related to the fruit growing and retailing or processing chain for automatic fruit identification and classifications in the future.

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Section: Comparison With Pre-trained DL Modelsmentioning

confidence: 99%

Fruit classification using attention-based MobileNetV2 for industrial applications

et al. 2022

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“…Sitaula et al [17] proposed multi-scale bag of visual world method based of deep features (MBoDVW). After generating codebooks and featurization, they classified with SVM on 4 datasets: CD1 (1,125 images, 3 classes), CD2 (1,638 images, 4 classes), CD3 (2,138 images, 5 classes) and CD4 (320 images, 4 classes).…”

Section: A X-ray Based Diagnosis Of Covid-19mentioning

confidence: 99%

A Review on Deep Learning based diagnosis of COVID-19 from X-ray and CT Images

Kumar

2022

2022 International Mobile and Embedded Technology Conference (MECON)

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More than 400 million cases of the new coronavirus (COVID-19) have been confirmed since December 2019 in more than 200 countries. Since the spread of original COVID-19 virus SARS-CoV-2, thousands of mutations have been discovered. The most dominant ones are Alpha, Beta, Gama, Delta and Omicron variants, with the Omicron variant rapidly spreading and dominating the current phase of the COVID wave across the globe. It needs early detection and self-isolation to contain the virus. Molecular tests like rRT-PCR are common for its detection. However, with the current spreading rate and lack of availability of large-scale testing laboratories, rapid diagnosis has become difficult. COVID-19 diagnosis from CT and X-ray images using deep learning techniques has been the subject of a lot of research in the last two years. This work presents a review of these studies sourced from top databases such as Web of Science and highlights challenges and research gaps with future research directions.

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“…CNN models have displayed state-of-the-art performances in many image classification tasks [ 39 , 40 , 41 , 42 ]. Although CNN-based approaches have achieved superior performance in various applications over the past decade, CNN models tend to predict labels with overconfidence [ 43 , 44 ].…”

Section: Literature Reviewmentioning

confidence: 99%

MixPatch: A New Method for Training Histopathology Image Classifiers

et al. 2022

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CNN-based image processing has been actively applied to histopathological analysis to detect and classify cancerous tumors automatically. However, CNN-based classifiers generally predict a label with overconfidence, which becomes a serious problem in the medical domain. The objective of this study is to propose a new training method, called MixPatch, designed to improve a CNN-based classifier by specifically addressing the prediction uncertainty problem and examine its effectiveness in improving diagnosis performance in the context of histopathological image analysis. MixPatch generates and uses a new sub-training dataset, which consists of mixed-patches and their predefined ground-truth labels, for every single mini-batch. Mixed-patches are generated using a small size of clean patches confirmed by pathologists while their ground-truth labels are defined using a proportion-based soft labeling method. Our results obtained using a large histopathological image dataset shows that the proposed method performs better and alleviates overconfidence more effectively than any other method examined in the study. More specifically, our model showed 97.06% accuracy, an increase of 1.6% to 12.18%, while achieving 0.76% of expected calibration error, a decrease of 0.6% to 6.3%, over the other models. By specifically considering the mixed-region variation characteristics of histopathology images, MixPatch augments the extant mixed image methods for medical image analysis in which prediction uncertainty is a crucial issue. The proposed method provides a new way to systematically alleviate the overconfidence problem of CNN-based classifiers and improve their prediction accuracy, contributing toward more calibrated and reliable histopathology image analysis.

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Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection

Cited by 35 publications

References 50 publications

Fruit classification using attention-based MobileNetV2 for industrial applications

Fruit classification using attention-based MobileNetV2 for industrial applications

A Review on Deep Learning based diagnosis of COVID-19 from X-ray and CT Images

MixPatch: A New Method for Training Histopathology Image Classifiers

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