Abstract
Brain tumour analysis without human involvement is a crucial field of study. Convolutional neural networks, on the other hand, excelled at solving computer vision and other challenges such as visual object recognition, detection, and segmentation. It aids in the diagnosis of brain tumours by improving brain pictures utilising segmentation algorithms that are extremely resistant to noise and cluster size sensitivity issues, as well as automated area of interest (ROI) detection. One of the key arguments for using CNNs is that they have a high level of accuracy and do not require human feature extraction. Detecting a brain tumour and correctly identifying its kind is a difficult undertaking. Because of its widespread use in image recognition, CCN performs better than others. Providing assistance to diagnose brain tumours becomes difficult if performed manually. Furthermore, it becomes difficult process when there is a huge amount of data to assist. Extracting tumour from the images becomes difficult. To overcome this drawback, the proposed method uses convolutional neural network-based model using MobileNet for detection of brain tumours given MRI images.
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Avanija, J. et al. (2023). Interpretation of Brain Tumour Using Deep Learning Model. In: Reddy, K.A., Devi, B.R., George, B., Raju, K.S., Sellathurai, M. (eds) Proceedings of Fourth International Conference on Computer and Communication Technologies. Lecture Notes in Networks and Systems, vol 606. Springer, Singapore. https://doi.org/10.1007/978-981-19-8563-8_33
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