Brain Tumor Detection Using the Inception Deep Learning Technique

Singamshetty, Ramyateja; Sruthi, Sangani; Chandhana, Kodati; Kollem, Sreedhar; Prasad, Ch. Rajendra

doi:10.1109/icrtec56977.2023.10111883

Cited by 9 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is important to make a compromise between the performance of the model and the size of the image input [21]. Typically, the input images are reduced to a fixed size that is computationally efficient for the deep learning model and large enough to retain important information [22] Learning the depth system used and the nature of the medical image being analysed can affect the sample size for the scaling of the input image [23].…”

Section: Resizingmentioning

confidence: 99%

Detection of Brain Tumour based on Optimal Convolution Neural Network

Kishore Kanna,

Sahoo,

Mandhavi

et al. 2024

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

INTRODUCTION: Tumours are the second most frequent cause of cancer today. Numerous individuals are at danger owing to cancer. To detect cancers such as brain tumours, the medical sector demands a speedy, automated, efficient, and reliable procedure. OBJECTIVES: Early phases of therapy are critical for detection. If an accurate tumour diagnosis is possible, physicians safeguard the patient from danger. In this program, several image processing algorithms are utilized. METHODS: Utilizing this approach, countless cancer patients are treated, and their lives are spared. A tumor is nothing more than a collection of cells that proliferate uncontrolled. Brain failure is caused by the development of brain cancer cells, which devour all of the nutrition meant for healthy cells and tissues. Currently, physicians physically scrutinize MRI pictures of the brain to establish the location and size of a patient's brain tumour. This takes a large amount of time and adds to erroneous tumour detection. RESULTS: A tumour is a development of tissue that is uncontrolled. Transfer learning may be utilized to detect the brain cancer utilizing. The model's capacity to forecast the presence of a cancer in a picture is its best advantage. It returns TRUE if a tumor is present and FALSE otherwise. CONCLUSION: In conclusion, the use of CNN and deep learning algorithms to the identification of brain tumor has shown remarkable promise and has the potential to completely transform the discipline of radiology.

show abstract

Section: Resizingmentioning

confidence: 99%