Deep Learning Model with VGG16 Model for Brain Tumour Detection

Author(s)	Mandeep Mehan, Dr. Harpreet Kaur, Dr. Neelofar Sohi
Country	India
Abstract	A mass or collection of abnormal brain cells is referred to as a brain tumour. The skull, in which the brain is housed, is exceedingly sturdy. There are several phases involved in the detection of brain tumours from biomedical images, including pre-processing, segmentation, feature extraction, and classification. The various schemes for the brain tumor detection are proposed in previous years but those schemes give low accuracy. In this paper, novel scheme is proposed which is based on transfer learning model. In the proposed scheme Parallel Non-Local Mean is used for the filtering and snake segmentation is used for the image segmentation. Transfer learning will be employed for classification in the final stage. The VGG16 and CNN models are combined to create the transfer learning model. Python will be used to implement the suggested model, and accuracy, precision, and recall will be evaluated of the outcomes.
Keywords	Brain Tumour, Snake Segmentation, Parallel Non-Local Mean Filter, VGG16, CNN
Field	Engineering
Published In	Volume 6, Issue 1, January-February 2024
Published On	2024-01-04
Cite This	Deep Learning Model with VGG16 Model for Brain Tumour Detection - Mandeep Mehan, Dr. Harpreet Kaur, Dr. Neelofar Sohi - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.11612
DOI	https://doi.org/10.36948/ijfmr.2024.v06i01.11612
Short DOI	https://doi.org/gtdsch

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