ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images

Ikechukwu, A. Victor; Murali, S.; Deepu, R; Shivamurthy, R. C.

doi:10.1016/j.gltp.2021.08.027

Cited by 158 publications

(78 citation statements)

References 21 publications

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“…Compared to the ResNet backbone, we discovered that the U-Net model with VGGNet achieved the best results. VGG outperforms ResNet for image segmentation tasks, according to [ 52 ]. The VGG model was chosen as the fixed feature extractor baseline.…”

Section: Resultsmentioning

confidence: 99%

Detecting Buildings and Nonbuildings from Satellite Images Using U-Net

Alsabhan

Alotaiby

Dudin

2022

Computational Intelligence and Neuroscience

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Automatic building detection from high-resolution satellite imaging images has many applications. Understanding socioeconomic development and keeping track of population migrations are essential for effective civic planning. These civil feature systems may also help update maps after natural disasters or in geographic regions undergoing dramatic population expansion. To accomplish the desired goal, a variety of image processing techniques were employed. They are often inaccurate or take a long time to process. Convolutional neural networks (CNNs) are being designed to extract buildings from satellite images, based on the U-Net, which was first developed to segment medical images. The minimal number of images from the open dataset, in RGB format with variable shapes, reveals one of the advantages of the U-Net; that is, it develops excellent accuracy from a limited amount of training material with minimal effort and training time. The encoder portion of U-Net was altered to test the feasibility of using a transfer learning facility. VGGNet and ResNet were both used for the same purpose. The findings of these models were also compared to our own bespoke U-Net, which was designed from the ground up. With an accuracy of 84.9%, the VGGNet backbone was shown to be the best feature extractor. Compared to the current best models for tackling a similar problem with a larger dataset, the present results are considered superior.

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Section: Resultsmentioning

confidence: 99%

Detecting Buildings and Nonbuildings from Satellite Images Using U-Net

Alsabhan

Alotaiby

Dudin

2022

Computational Intelligence and Neuroscience

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“…Interestingly, no study to date has examined residual NNs (ResNets), 37 which are a variation on CNN networks that typically generalize to the data better than their predecessor. ResNet 37 was first proposed in 2016 and has been widely used in image processing tasks such as diagnosing pneumonia from x-ray 38 and Alzheimer’s disease from MRI. 39…”

Section: Discussionmentioning

confidence: 99%

Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review

Baker

Kandasamy

2022

Pediatr Res

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Background Machine learning has been attracting increasing attention for use in healthcare applications, including neonatal medicine. One application for this tool is in understanding and predicting neurodevelopmental outcomes in preterm infants. In this study, we have carried out a systematic review to identify findings and challenges to date. Methods This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Four databases were searched in February 2022, with articles then screened in a non-blinded manner by two authors. Results The literature search returned 278 studies, with 11 meeting the eligibility criteria for inclusion. Convolutional neural networks were the most common machine learning approach, with most studies seeking to predict neurodevelopmental outcomes from images and connectomes describing brain structure and function. Studies to date also sought to identify features predictive of outcomes; however, results varied greatly. Conclusions Initial studies in this field have achieved promising results; however, many machine learning techniques remain to be explored, and the consensus is yet to be reached on which clinical and brain features are most predictive of neurodevelopmental outcomes. Impact This systematic review looks at the question of whether machine learning can be used to predict and understand neurodevelopmental outcomes in preterm infants. Our review finds that promising initial works have been conducted in this field, but many challenges and opportunities remain. Quality assessment of relevant articles is conducted using the Newcastle–Ottawa Scale. This work identifies challenges that remain and suggests several key directions for future research. To the best of the authors’ knowledge, this is the first systematic review to explore this topic.

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“…The Last fully connected layer uses softmax layer for classification purpose. [16] The Inception V3 is a deep learning model based on Convolutional Neural Networks, which is used for image classification. The model itself is made up of symmetric and asymmetric building blocks, including convolutions, average pooling, max pooling, concatenations, dropouts, and fully connected layers.…”

Section: Deep Learning Proposed Modelsmentioning

confidence: 99%

Deep Chest: Computer Vision and Deep Learning for Lung Disease Detection

T.O¹,

V.P²

2022

IJETMS

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Lung disease is one of the most common medical conditions in the world. Pulmonary diseases happen when there are problems with any part of the lungs. Lung infections can affect the air ways of respiratory function, and it may affect the ability to breathe and the good functionality of the human respiratory system. Studying different lung diseases and categorizing them is one of the most interesting areas of research in recent years. Deep learning techniques in the field of computer vision along with medical engineering have recently achieved impressive results. In this work we used three deep learning models for classifying the lung diseases and analyzed the result to find the best model among them. The obtained result shows higher validation accuracy for the deep learning models and this lead to conclude that the method is suitable for predicting Lung diseases.

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ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images

Cited by 158 publications

References 21 publications

Detecting Buildings and Nonbuildings from Satellite Images Using U-Net

Detecting Buildings and Nonbuildings from Satellite Images Using U-Net

Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review

Deep Chest: Computer Vision and Deep Learning for Lung Disease Detection

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