Review of Brain Tumor Detection Using Various Techniques

Kaur, Harminder; Gill, Amninder Kaur

doi:10.23956/ijarcsse/v7i4/0221

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…As previously discussed, [11,12,18,24], a brain tumour is a clumpy growth of living and dead brain cells. The prognosis for people with brain tumours varies according on the type and stage of the disease.…”

Section: Brain Tumormentioning

confidence: 94%

Classification and Detection of Brain Tumors by Aquila Optimizer Hybrid Deep Learning Based Latent Features with Extreme Learner

Agrawal¹

2023

ITM Web Conf.

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Brain cancer is a potentially fatal illness that affects the brain. To preserve lives, early tumour detection is now crucial. Imaging in medicine is one method for diagnosing brain tumours. To help find brain tumours, researchers are turning to deep learning. Error in individual early diagnosis of the condition has been demonstrated to be reduced using deep learning algorithms. In the case of brain tumours, even a slight misdiagnosis might have serious consequences. When it comes to processing medical images, spotting brain tumours is still a difficult task. It’s difficult to spot the tumour on a brain scan. The precision of the image is impacted by many disturbances and a delay. We used deep learning methods to describe brain disorders in our paper. Brain disease detection utilizing deep learning methods is related to the study of new information. Proposed TL-based DenseNet121 model achieved accuracy, sensitivity, specificity, F1Score, precision, and IoU of 98.38, 97.33, 99.1, 98.23, 98.62, and 96.62 respectively. The results obtained on the brain tumor data set demonstrate that the proposed method outperforms others in terms of F1-score, Precision, Sensitivity, Accuracy, Specificity, and IoU.

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Section: Brain Tumormentioning

confidence: 94%

Classification and Detection of Brain Tumors by Aquila Optimizer Hybrid Deep Learning Based Latent Features with Extreme Learner

Agrawal¹

2023

ITM Web Conf.

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“…Unlike cryptography, which encrypts a message, steganography hides the message in plain sight, making it less likely to be detected. This can be particularly useful in situations where secrecy is paramount, such as in military or intelligence operations, where even the knowledge that a message is being sent can be dangerous [8].…”

Section: Advantage Of Steganographymentioning

confidence: 99%

Image hiding by using spatial domain steganography

Sulong

Wimmer²

2023

WJCMS

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This article provides an overview of steganography and its use for hiding images in other images. Steganography is a technique that allows users to hide information in plain sight, making it difficult for unauthorized parties to detect or access the information. Spatial domain steganography is a popular technique for hiding images within other images, where the least significant bits of the cover image are modified to embed the secret image. The article discusses the advantages of steganography and its use in various applications such as digital watermarking and secure communication. The article also provides an overview of the various techniques used for spatial domain steganography, and how these techniques can be implemented using programming languages such as Python. Finally, the article concludes by emphasizing the importance of using steganography responsibly and ethically.

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“…Edge-based methods: These methods are often used for real-time applications, as they are computationally effi-cient and can work well in low light conditions [14]. non-maximum suppression and hysteresis thresholding to produce the final edge map.…”

Section: )mentioning

confidence: 99%

Object detection in inland vessels using combined trained and pretrained models of YOLO8

Goudah,

Jarofka,

El-Habrouk

et al. 2023

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One of the central challenges within the domain of computer vision is object detection, encompassing the identification and localization of specific entities within an image. Introducing a pioneering approach, the YOLO (You Only Look Once) algorithm emerged in 2015, executing object detection within a singular neural network. This innovation triggered a profound transformation within object detection, ushering in remarkable advancements beyond the capacities of the preceding decade. Subsequently, YOLO underwent successive iterations, culminating in eight versions that have earned prominent stature among leading object identification algorithms. This recognition is attributed to YOLO's integration of state-of-the-art concepts prevalent in the realm of computer vision research. Particularly noteworthy is the latest iteration, YOLOv8, which demonstrates superior performance in terms of both accuracy and speed when juxtaposed with YOLOv7 and YOLOv5. This study delves into the most recent strides in object detection as an important field of computer vision, which has been seamlessly assimilated into YOLOv5, YOLOv7, YOLOv8, and their antecedents. The introductory section, delineating the foundational importance of object detection, aligns seamlessly with the research's overall narrative. The elucidation of object detection's significance within diverse contexts, such as vehicle identification across varying scales and environments, underscores its multifaceted utility. The refinement process further enhances the discernment of YOLO's progression through its iterations, elucidating the evolution from the pre-eminent YOLOv1 to the recent apex represented by YOLOv8. Notably, the text now highlights YOLOv8's distinc-tive advancements in accuracy and speed over YOLOv7 and YOLOv5, lending heightened clarity to the incremental evolution of the algorithm. The augmentation extends to the exploration of YOLOv8's amalgamation with contemporary computer vision concepts. These concepts' incorporation is now underscored, demonstrating how YOLOv8 benefits from the strides made in computer vision research. The final passage captures the thrust of the research, examining the application of the developed object detection models within the specific context of inland waterway vessels. The distinct stages of detection, the addition of new classes, manual annotation, and the process of network training are now presented with greater precision,

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Review of Brain Tumor Detection Using Various Techniques

Cited by 6 publications

References 3 publications

Classification and Detection of Brain Tumors by Aquila Optimizer Hybrid Deep Learning Based Latent Features with Extreme Learner

Classification and Detection of Brain Tumors by Aquila Optimizer Hybrid Deep Learning Based Latent Features with Extreme Learner

Image hiding by using spatial domain steganography

Object detection in inland vessels using combined trained and pretrained models of YOLO8

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