Application on Reinforcement Learning for Diagnosis Based on Medical Image

Magalhães, Stelmo; Netto, Barros; Rodrigues, Véra; Leite, Coelho; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de; Neto, Areolino de Almeida

doi:10.5772/5291

Cited by 10 publications

(5 citation statements)

References 39 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, an autoencoder input codes the stimuli-gathered codings and reconstructs from these the output; in this case, the output must be as close as possible to the input information; restricted Boltzmann machines are composed of visible and hidden layers that reconstruct the input estimating the probability distribution of the original input; in deep belief network, the output of a restricted Boltzmann machine is the input of another Boltzmann machine; finally, generative adversarial networks are generative models that are composed of two competing CNNs: the first CNN generates artificial training images, and the second CNN discriminates real training images from artificial ones; the desired expectation is that the discriminator cannot tell the difference between the two images; this algorithm is very promising for medical image analyses; other unsupervised methods are clustering, which can be used to divide data in groups, and principal component analysis that reduces data dimension without losing critical information and then creating groups. • Reinforcement learning: In this case, learning is enhanced with a reward when the machine executes a "winning" choice; similarly, Q-learning algorithm (Rodrigues et al 2008) allows to compute the future rewards when the machine is performing a certain action in a particular state in order to keep on acting in an optimal manner. • Recommender system: In this case, the online user customizes a site as what happens in e-commerce.…”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

“…Autoencoders have been used in research to detect lesion with breast histology images, predict risk for cognitive deficits and classify lung and breast lesions, whereas stacked autoencoders have been applied for segmentation and image enhancement/generation. Also other algorithms are applied in radiology, for instance, reinforcement learning in combination with data mining helps in decision-making for physicians in cancer diagnosis, for segmentation tasks and for classification of lung nodules (Rodrigues et al 2008). A critical element of these technologies is that before applying AI to medical imaging and more broadly to healthcare system, algorithms need to be trained with data derived from clinical activities and in different forms; for example, 1.2 million of training data are being used to teach DL algorithms on MRI brain imaging; by the way, the quality of DL techniques depends on the quality of training data; this issue can be improved by adopting multisite, standardized and methodologically adequate acquisition protocols (Jiang et al 2017).…”

Section: Medical Imaging and Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

P5 eHealth: An Agenda for the Health Technologies of the Future

Pravettoni¹,

Triberti²

2020

View full text Add to dashboard Cite

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Section: Medical Imaging and Machine Learningmentioning

confidence: 99%

P5 eHealth: An Agenda for the Health Technologies of the Future

Pravettoni¹,

Triberti²

2020

View full text Add to dashboard Cite

Section: Medical Imaging and Machine Learningmentioning

confidence: 99%

Predictive Precision Medicine: Towards the Computational Challenge

Tuena

Semonella

Fernández‐Álvarez

et al. 2019

P5 eHealth: An Agenda for the Health Technologies of the Future

View full text Add to dashboard Cite

The emerging fields of predictive and precision medicine are changing the traditional medical approach to disease and patient. Current discoveries in medicine enable to deepen the comprehension of diseases, whereas the adoption of high-quality methods such as novel imaging techniques (e.g. MRI, PET) and computational approaches (i.e. machine learning) to analyse data allows researchers to have meaningful clinical and statistical information. Indeed, applications of radiology techniques and machine learning algorithms rose in the last years to study neurology, cardiology and oncology conditions. In this chapter, we will provide an overview on predictive precision medicine that uses artificial intelligence to analyse medical images to enhance diagnosis, prognosis and treatment of diseases. In particular, the chapter will focus on neurodegenerative disorders that are one of the main fields of application. Despite some critical issues of this new approach, adopting a patient-centred approach could bring remarkable improvement on individual, social and business level.

show abstract

“…Reinforcement learning has achieved very success in recent years in the following artificial intelligence applications: robot control, computer vision, autonomous driving, and computer gaming. And also, has emerged as one of the crucial areas in the field of artificial intelligence impacting the field of health care including diagnosis, prognosis, and other medical treatments [13].…”

Section: Introductionmentioning

confidence: 99%

A New Approach using Deep Learning and Reinforcement Learning in HealthCare

Yousra¹,

Abdelhakim²,

Mohamed³

2023

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

Nowadays, skin cancer is one of the most important problems faced by the world, due especially to the rapid development of skin cells and excessive exposure to UV rays. Therefore, early detection at an early stage employing advanced automated systems based on AI algorithms plays a major job in order to effectively identifying and detecting the disease, reducing patient health and financial burdens, and stopping its spread in the skin. In this context, several early skin cancer detection approaches and models have been presented throughout the last few decades to improve the rate of skin cancer detection using dermoscopic images. This work proposed a model that can help dermatologists to know and detect skin cancer in just a few seconds. This model combined the merits of two major artificial intelligence algorithms: Deep Learning and Reinforcement Learning following the great success we achieved in the classification and recognition of images and especially in the medical sector. This research included four main steps. Firstly, the pre-processing techniques were applied to improve the accuracy, quality, and consistency of a dataset. The input dermoscopic images were obtained from the HAM10000 database. Then, the watershed algorithm was used for the segmentation process performed to extract the affected area. After that, the deep convolutional neural network (CNN) was utilized to classify the skin cancer into seven types: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma melanocytic nevi, melanoma vascular skin lesions. Finally, in regards to the reinforcement learning part, the Deep Q_Learning algorithm was utilized to train and retrain our model until we found the best result. The accuracy metric was utilized to evaluate the efficacy and performance of the proposed method, which achieved a high accuracy of 80%. Furthermore, the experimental results demonstrate how reinforcement learning can be effectively combined with deep learning for skin cancer classification tasks.

show abstract

Application on Reinforcement Learning for Diagnosis Based on Medical Image

Cited by 10 publications

References 39 publications

P5 eHealth: An Agenda for the Health Technologies of the Future

P5 eHealth: An Agenda for the Health Technologies of the Future

Predictive Precision Medicine: Towards the Computational Challenge

A New Approach using Deep Learning and Reinforcement Learning in HealthCare

Contact Info

Product

Resources

About