Abstract: Brain tumors are one of the most threatening types of tumors in the world. Magnetic Resonance Imaging (MRI), a popular non-invasive strategy, produces a large and diverse number of tissue contrasts in each imaging modality and has been widely used by medical specialists to diagnose brain tumors. However, the manual segmentation and analysis of structural MRI images of brain tumors is an arduous and time-consuming task which, thus far, can only be performed by a professional neurologist. Our project aims to simplify this process with the help of machine learning algorithms so as to efficiently detect brain tumors in an MRI using a computer. In this project we use two different machine learning models to compare their success and loss rates and to also identify which algorithm performs better. The two machine learning algorithms being used are LeNet-5 and a self-designed model of convolutional neural networks (CNN). The loss rates of the two models can be compared through our project. In a recent study, researchers developed a model based on deep learning to analyze data. The model used inputs from a psychological questionnaire to estimate an individual’s psychological age and so on. We believe this project will have a great significance in the coming future not only in relevance in the medical industry but in the technological industry as well.

PDF | DOI: 10.17148/IJARCCE.2023.12107

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