Abstract
According to the American Cancer Society, cancers related to the brain and nervous system are ranked as the tenth leading cause of mortality in humans. In addition to this, the World Health Organization (WHO) reports that low-income nations are experiencing a lack of neurologists, who play an essential part in the functioning of the healthcare sector. There is currently no method that is reliable enough to permit the classification of brain illnesses into multiple classes. The multi-class classification of clinical brain images was made possible by our machine learning approach, which we proposed. The classification of brain disorders, including Alzheimer’s disease, dementia, brain cancer, epilepsy, stroke, and Parkinson’s disease, would be accomplished using a deep learning-based convolutional neural network (CNN). The Visual Geometry Group-16 (VGG-16) architecture was taken into consideration throughout the feature selection process, and the Adam optimizer was used to perfect the model. The proposed CNN model would be beneficial in alleviating the arduous labor of neurologists.
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Pasha, Z., Parthasarathy, S., Jayaraman, V., Lakshminarayan, A.R. (2023). Prediction of Brain Diseases Using Machine Learning Models: A Survey. In: Bindhu, V., Tavares, J.M.R.S., Vuppalapati, C. (eds) Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems . Lecture Notes in Electrical Engineering, vol 977. Springer, Singapore. https://doi.org/10.1007/978-981-19-7753-4_74
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