Brain Tumour Detection Using Deep Learning

Sinha, Avigyan; Aneesh, R P; Suresh, M.; R, Nitha Mohan; Abinaya, D; Singerji, None Ashwin

doi:10.1109/icbsii51839.2021.9445185

Cited by 8 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To provide adequate treatment, the bed's hospital must provide the necessary intensive treatment beds. [6][7][8] 2/3 of the medical specialists (75.6%, n = 223) are unaware of whether their hospital plans to use additional intensive care beds. The highest percentage of medical specialists who are informed is observed among managers, 49.2% (n = 12) (p = 0.006 χ2 = 12.34).…”

Section: Resultsmentioning

confidence: 99%

“…The number of nurses needs to correspond to the number of intensive care beds, which requires the secondment of nurses from other wards to the intensive care unit. [7,8] The distribution and secondment of nurses are coordinated and led by managers. To be able to make adequate management decisions related to the treatment of the most severely injured, managers must know the provision of intensive care beds.…”

Section: Resultsmentioning

confidence: 99%

“…[4,5] Of the total flow of patients to be admitted to the hospital, those in triage categories T1 and T4 must be immediately sent by the sorting medic to the shock room, intensive care unit or operating rooms, which implies the hospital's ability to adapt to the new requirements and redirect medical capabilities to some of the wards and units. [6][7][8]…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Immediate Bed Availability in Case of a Disaster

Georgieva¹,

Kostadinov²,

Semerdjieva³

2022

JofIMAB

View full text Add to dashboard Cite

Introduction: During a disaster, an increase in the number of casualties requiring hospital admission is recorded. This challenge the hospitals' management - there is a need for beds, rooms, consumables, drugs, and medical teams to support the increased number of patients arriving. Purpose: This study aims to analyze the awareness of hospital medical professionals about the required procedures described in the hospital contingency plan for increased bed availability in case of disaster. Materials and methods: 295 hospital medical professionals in the Plovdiv region, Bulgaria, participated in an anonymous survey that contained 55 questions. Results: Most of the respondents are not familiar with the approved procedures to optimize the availability of emergency beds after the disaster that impacts the hospital disaster resilience. Conclusions: Optimization of activities related to operational hospital disaster resilience must include training of medical specialists in immediate bed availability procedures.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Immediate Bed Availability in Case of a Disaster

Georgieva¹,

Kostadinov²,

Semerdjieva³

2022

JofIMAB

View full text Add to dashboard Cite

show abstract

“…The numerical results indicate that the brain's aberrant and normal tissues may be distinguished with around 98.51% accuracy. Pictures from magnetic resonance show how effective the technology is [5][6][7].…”

Section: Related Workmentioning

confidence: 99%

The Evaluation of 2D and EfficientB0 Convolution Networks for detecting Brain tumor based on MRI images

Subbarayudu,

Vijendar Reddy,

Keerthi

et al. 2024

MATEC Web Conf.

View full text Add to dashboard Cite

Brain tumors represent a significant healthcare challenge, affecting both children and adults with potentially aggressive consequences. Accounting for a substantial percentage of all primary Central Nervous System (CNS) tumors, brain tumors pose a substantial burden, with approximately 11,700 new diagnoses annually. The classification of brain tumors into benign, malignant, pituitary, and other types necessitates precise diagnostic techniques and treatment planning to enhance patient life expectancy. Traditionally, the detection of brain tumors relied on the expertise of specialists analyzing Magnetic Resonance Images (MRI) without the aid of advanced technology. MRI remains the gold standard for brain tumor detection, generating vast amounts of image data for radiologists to interpret. Manual examinations, however, carry a risk of errors due to the intricacies and diverse properties of brain tumors, potentially leading to delayed treatment and, tragically, loss of lives. In this context, the application of automated classification techniques using Machine Learning (ML) and Deep Learning (DL) has emerged as a promising solution. These techniques, primarily employing Deep Learning Algorithms such as Convolutional Neural Networks (CNN) like 2D-convolutions and Deep Learning Models like ResNet50 and EfficientNetB0, in addition to traditional Machine Learning algorithms like Support Vector Machines (SVM), have consistently demonstrated superior accuracy in brain tumor detection compared to manual prediction. These automated methods have consistently exhibited superior accuracy in the detection and classification of brain tumors compared to manual approaches. This research proposes a robust system for the early detection and accurate classification of brain tumors, leveraging the power of Deep Learning and Machine Learning. By incorporating state-of-the-art techniques, this system aims to empower medical professionals worldwide in identifying brain tumors at their earliest stages, ultimately leading to more timely and effective treatments. Such advancements hold great promise in reducing the human suffering associated with brain tumors and improving patient outcomes.

show abstract

“…This study compared 28 peer-reviewed papers and emphasized the various methodologies. Sinha et al [6] pinpoints the erroneous image and tumor area in the brain. It also identifies the density of segmented tumor, which may be approximated using the mask.…”

Section: Literature Studymentioning

confidence: 99%

Brain Tumor Detection Using Deep Learning

Jairam

Lokeshwar

Divya

et al. 2023

Advances in Science and Technology

View full text Add to dashboard Cite

Brain tumors are developed as a result of unregulated and fast cell proliferation. It may result in death if not treated in the early stages. The imaging technology used to diagnose brain tumors is known as magnetic resonance imaging (MRI). Early detection of brain tumors is critical in medical practise in order to determine whether the tumor will progress to malignancy. For picture categorization, deep learning is a useful and effective method. Deep learning has been widely used in a variety of sectors, including medical imaging, because its application does not necessitate the expertise of a subject matter expert, but does necessitate a large amount of data and a variety of data in order to produce accurate classification results. The deep learning technique for image categorization is the convolutional neural network (CNN).In this research work , two different models are used to categorize brain tumors and their results were evaluated using performance metrics like accuracy and precision and the results were impressive

show abstract

Brain Tumour Detection Using Deep Learning

Cited by 8 publications

References 15 publications

Immediate Bed Availability in Case of a Disaster

Immediate Bed Availability in Case of a Disaster

The Evaluation of 2D and EfficientB0 Convolution Networks for detecting Brain tumor based on MRI images

Brain Tumor Detection Using Deep Learning

Contact Info

Product

Resources

About