MRI Tumor Detection and Localization by Multiple Threshold Object Counting Technique

Nasor, Mohamed; Obaid, Walid

doi:10.1109/comapp.2018.8460322

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…The core element in the method we developed for the detection of brain tumours is the object counting technique which is widely used in computer vision for various applications such as, face detection, vehicle detection, product counting, pedestrian counting, and security systems [16,17]. However, using object counting algorithm alone will not give a reliable detection of the true tumours in MRI images due to the very close similarity between brain tumours and other brain structures.…”

Section: Methodsmentioning

confidence: 99%

Detection and localisation of multiple brain tumours by object counting and elimination

Nasor

Obaid

2020

IET Image Processing

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Section: Methodsmentioning

confidence: 99%

Detection and localisation of multiple brain tumours by object counting and elimination

Nasor

Obaid

2020

IET Image Processing

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“…Furthermore, the computations to detect tumors are not explained. A "multiple threshold object counting" technique is proposed for detecting brain tumors [20]. The technique claims to be able to detect brain tumors with good accuracy.…”

Section: Dataset and Related Workmentioning

confidence: 99%

Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning

Qodri

Soesanti

Nugroho

2021

IJITEE

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Tumors are cells that grow abnormally and uncontrollably, whereas brain tumors are abnormally growing cells growing in or near the brain. It is estimated that 23,890 adults (13,590 males and 10,300 females) in the United States and 3,540 children under the age of 15 would be diagnosed with a brain tumor. Meanwhile, there are over 250 cases in Indonesia of patients afflicted with brain tumors, both adults and infants. The doctor or medical personnel usually conducted a radiological test that commonly performed using magnetic resonance image (MRI) to identify the brain tumor. From several studies, each researcher claims that the results of their proposed method can detect brain tumors with high accuracy; however, there are still flaws in their methods. This paper will discuss the classification of MRI-based brain tumors using deep learning and transfer learning. Transfer learning allows for various domains, functions, and distributions used in training and research. This research used a public dataset. The dataset comprises 253 images, divided into 98 tumor-free brain images and 155 tumor images. Residual Network (ResNet), Neural Architecture Search Network (NASNet), Xception, DenseNet, and Visual Geometry Group (VGG) are the techniques that will use in this paper. The results got to show that the ResNet50 model gets 96% for the accuracy, and VGG16 gets 96% for the accuracy. The results obtained indicate that transfer learning can handle medical images.

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MRI brain tumor detection based on soft computing

Hamzah¹,

Mhaidi²

2023

1st International Conference on Achieving the Sustainable Development Goals

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MRI Tumor Detection and Localization by Multiple Threshold Object Counting Technique

Cited by 4 publications

References 8 publications

Detection and localisation of multiple brain tumours by object counting and elimination

Detection and localisation of multiple brain tumours by object counting and elimination

Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning

MRI brain tumor detection based on soft computing

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