Impact of Convolutional Neural Network Hidden Layers on Brain Tumor Detection

Cancers such as lung, breast, melanoma, colon, and kidney can often metastasize to the brain. Managing therapy and medical care might become difficult when these metastases occur in addition to the underlying disease. Unlike neurons, neuroglia, also known as glial cells, are four types of supporting cells that make up the majority of brain cells in the brain. Tumors arising from glial cells are referred to as gliomas, or glioblastomas. They have a variety of forms, including astrocytomas from astrocytes and ependymomas from ependymal cells. Convolutional Neural Networks (CNNs) were used in a recent study to identify brain cancers and classify them into four groups: pituitary, meningioma, glioma, and no tumor. Parameters such as recall, precision, and F1 score were evaluated. This study is unique in that it employs a variety of assessment criteria to examine the effects of various brain tumors and the role played by CNN hidden layers. Furthermore, the built model helps to forecast particular tumors from the dataset, which is helpful for radiologists and can be used as a tool to get second opinion.