Deep Reinforcement Learning Classification of Brain Tumors on MRI

Stember, Joseph N.; Shalu, Hrithwik

doi:10.1007/978-981-19-3440-7_11

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…However, once the model score reaches a threshold of 0.9, its predictive power begins to decline. The findings corroborate the notion that correct results may be obtained quickly using feature tagging of home films utilising machine learning categorization of autism [14]. After some time, McNamara et al, [15] also categorize the same dataset using Decision Tree and arbitrary forest classifiers while considering enhanced data pre-processing, in which the authors eliminate least significant characteristics and records with missing values.…”

Section: Literature Surveysupporting

confidence: 68%

Developing an Artificial Intelligence Based Model for Autism Spectrum Disorder Detection in Children

Chitta Hrudaya Neeharika,

Yeklur Mohammed Riyazuddin

2023

ARASET

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Sensory difficulties, such as an over or under responsiveness to noises, smells, or touch, are frequently present in individuals with Autism Spectrum Disease (ASD), a neurodevelopmental disorder. The condition's primary cause is hereditary, however early diagnosis and therapy can assist. Traditional clinical procedures may be expensive and time consuming, but in current history, deep learning based sophisticated diagnosis has emerged to supplement them. The goal of this study is to streamline the diagnostic procedure by identifying the most important characteristics and automating them using existing classification methods. We have looked at datasets including toddlers, kids, teens, and adults with autism spectrum disorder. To find the highest performing classification and feature set for these four ASD datasets, we compared state-of-the-art categorization and feature selection methods. Across datasets of toddlers, kids, teens, and adults, our experiments reveal that the multilayer perceptron (MLP) classifier achieves 100% accuracy with the fewest possible features. We also determine that the proposed feature selection approach ranks the most important characteristics the highest across all four ASD datasets.

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Section: Literature Surveysupporting

confidence: 68%

Developing an Artificial Intelligence Based Model for Autism Spectrum Disorder Detection in Children

Chitta Hrudaya Neeharika,

Yeklur Mohammed Riyazuddin

2023

ARASET

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“…They highlighted the role of some characteristics such as data size, gold standard, DL architecture, evaluation parameters, scientific validation, and clinical evaluation ( 56 ) in developing robust AI models. For instance, deep reinforcement learning and deep neuroevolution models have generalized well based on sparse data and successfully used for the evaluation of treatment response in brain metastasis and classification of brain tumors using MR images ( 75 , 76 ). Nonetheless, for the time being, some assessments of unusual conditions may not be ethically researched and evaluated by AI, due to the limited availability of copious patient data.…”

Section: Case Numbersmentioning

confidence: 99%

Artificial intelligence in neuroradiology: a scoping review of some ethical challenges

Khosravi

Schweitzer

2023

Front. Radiol.

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Artificial intelligence (AI) has great potential to increase accuracy and efficiency in many aspects of neuroradiology. It provides substantial opportunities for insights into brain pathophysiology, developing models to determine treatment decisions, and improving current prognostication as well as diagnostic algorithms. Concurrently, the autonomous use of AI models introduces ethical challenges regarding the scope of informed consent, risks associated with data privacy and protection, potential database biases, as well as responsibility and liability that might potentially arise. In this manuscript, we will first provide a brief overview of AI methods used in neuroradiology and segue into key methodological and ethical challenges. Specifically, we discuss the ethical principles affected by AI approaches to human neuroscience and provisions that might be imposed in this domain to ensure that the benefits of AI frameworks remain in alignment with ethics in research and healthcare in the future.

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Evolutionary Strategies AI Addresses Multiple Technical Challenges in Deep Learning Deployment: Proof-of-Principle Demonstration for Neuroblastoma Brain Metastasis Detection

Purkayastha,

Shalu,

Gutman

et al. 2024

J Digit Imaging. Inform. med.

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Deep Reinforcement Learning Classification of Brain Tumors on MRI

Cited by 6 publications

References 18 publications

Developing an Artificial Intelligence Based Model for Autism Spectrum Disorder Detection in Children

Developing an Artificial Intelligence Based Model for Autism Spectrum Disorder Detection in Children

Artificial intelligence in neuroradiology: a scoping review of some ethical challenges

Evolutionary Strategies AI Addresses Multiple Technical Challenges in Deep Learning Deployment: Proof-of-Principle Demonstration for Neuroblastoma Brain Metastasis Detection

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