Skin Cancer Classification Using Inception Network and Transfer Learning

Benedetti, Priscilla; Perri, Damiano; Simonetti, Marco; Gervasi, Osvaldo; Reali, Gianluca; Femminella, Mauro

doi:10.1007/978-3-030-58799-4_39

Cited by 19 publications

(12 citation statements)

References 21 publications

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“…In extremely computationally complicated domains such as chemistry, mathematics, and scientific modelling, artificial intelligence technology is fundamental. It is also useful for detecting certain patterns in photos and other data aggregation, such as carcinoma detection, molecular structures, and emotion recognition from images and photographs [9][10][11][12][13]. The mathematical framework must automatically, and in some cases intelligently, adapt to circumstances in which the system variables are large and vary fast [14,15].…”

Section: Related Workmentioning

confidence: 99%

Deploying Efficiently Modern Applications on Cloud

2022

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This study analyses some of the leading technologies for the construction and configuration of IT infrastructures to provide services to users. For modern applications, guaranteeing service continuity even in very high computational load or network problems is essential. Our configuration has among the main objectives of being highly available (HA) and horizontally scalable, that is, able to increase the computational resources that can be delivered when needed and reduce them when they are no longer necessary. Various architectural possibilities are analysed, and the central schemes used to tackle problems of this type are also described in terms of disaster recovery. The benefits offered by virtualisation technologies are highlighted and are bought with modern techniques for managing Docker containers that will be used to build the back-end of a sample infrastructure related to a use-case we have developed. In addition to this, an in-depth analysis is reported on the central autoscaling policies that can help manage high loads of requests from users to the services provided by the infrastructure. The results we have presented show an average response time of 21.7 milliseconds with a standard deviation of 76.3 milliseconds showing excellent responsiveness. Some peaks are associated with high-stress events for the infrastructure, but the response time does not exceed 2 s even in this case. The results of the considered use case studied for nine months are presented and discussed. In the study period, we improved the back-end configuration and defined the main metrics to deploy the web application efficiently..

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Section: Related Workmentioning

confidence: 99%

Deploying Efficiently Modern Applications on Cloud

2022

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Section: Related Workmentioning

confidence: 99%

Deploying Efficiently Modern Applications on Cloud

Perri¹,

Simonetti²,

Gervasi³

2022

Preprint

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This study analyses some of the leading technologies for the construction and configuration of IT infrastructures to provide services to users. For modern applications, guaranteeing service continuity even in very high computational load or network problems is essential. Our configuration has among the main objectives of being highly available (HA) and horizontally scalable, that is, able to increase the computational resources that can be delivered when needed and reduce them when they are no longer necessary. Various architectural possibilities are analysed, and the central schemes used to tackle problems of this type are also described in terms of disaster recovery. The benefits offered by virtualisation technologies are highlighted and are bought with modern techniques for managing Docker containers that will be used to build the back-end of a sample infrastructure related to a use-case we have developed. In addition to this, an in-depth analysis is reported on the central autoscaling policies that can help manage high loads of requests from users to the services provided by the infrastructure. The results we have presented show an average response time of 21.7 milliseconds with a standard deviation of 76.3 milliseconds showing excellent responsiveness. Some peaks are associated with high-stress events for the infrastructure, but the response time does not exceed 2 seconds even in this case. The results of the considered use case studied for nine months are presented and discussed. In the study period, we improved the back-end configuration and defined the main metrics to deploy the web application efficiently.

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“…Another problem deriving from the use of some particular types of datasets is the imbalance of the classes in the classification process (unbalanced classes) [12,13]. This phe-nomenon was shown to have a negative impact on traditional classifier training.…”

Section: Related Workmentioning

confidence: 99%

Synthetic Data Generation to Speed-Up the Object Recognition Pipeline

2021

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This paper provides a methodology for the production of synthetic images for training neural networks to recognise shapes and objects. There are many scenarios in which it is difficult, expensive and even dangerous to produce a set of images that is satisfactory for the training of a neural network. The development of 3D modelling software has nowadays reached such a level of realism and ease of use that it seemed natural to explore this innovative path and to give an answer regarding the reliability of this method that bases the training of the neural network on synthetic images. The results obtained in the two proposed use cases, that of the recognition of a pictorial style and that of the recognition of men at sea, lead us to support the validity of the approach, provided that the work is conducted in a very scrupulous and rigorous manner, exploiting the full potential of the modelling software. The code produced, which automatically generates the transformations necessary for the data augmentation of each image, and the generation of random environmental conditions in the case of Blender and Unity3D software, is available under the GPL licence on GitHub. The results obtained lead us to affirm that through the good practices presented in the article, we have defined a simple, reliable, economic and safe method to feed the training phase of a neural network dedicated to the recognition of objects and features to be applied to various contexts.

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Skin Cancer Classification Using Inception Network and Transfer Learning

Cited by 19 publications

References 21 publications

Deploying Efficiently Modern Applications on Cloud

Deploying Efficiently Modern Applications on Cloud

Deploying Efficiently Modern Applications on Cloud

Synthetic Data Generation to Speed-Up the Object Recognition Pipeline

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