A Comparative Study for Brain Tumor Detection Analysis using CNN and VGG-16 and its Application

Authors : Alphonse MUGABIRE Masters; Dr. Wilson Musoni

Volume/Issue : Volume 8 - 2023, Issue 9 - September

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://tinyurl.com/3hkpxna4

DOI : https://doi.org/10.5281/zenodo.8378865

The incidence of brain tumors, a highly malignant form of cancer, is widespread around the world, affecting millions of individuals. Early detection plays a crucial role in saving lives, but the process of identifying and classifying tumor types accurately requires reviewing numerous MRI images. Deep learning models have the capability to handle such large datasets and provide precise results. However, it is important to note that the outcomes produced by deep learning models can vary depending on the dataset used. This comparative study focused on evaluating the effectiveness of deep learning models on two distinct Magnetic Resonance Imaging (MRI) brain tumor datasets. The goal of this research was to identify the best deep learning model that can achieve the highest accuracy in detecting brain tumors compared to others in the dataset. The models were individually applied to pre- processed datasets to extract features from the MRI images. Segmentation of tumor regions can be challenging due to the visual similarity between normal tissue and brain tumor cells. Therefore, an automatic tumor detection approach with high accuracy is necessary. To train our algorithm effectively, a diverse range of MRI images with different tumor sizes, locations, shapes, and intensities was utilized. We employed "TensorFlow" and "Keras" frameworks within the programming language "Python" to develop our optimal solution, as this language provides efficient functionality for rapid implementation. As part of the research process, a comprehensive literature review was conducted, and secondary data was collected. Performance metrics were employed for data analysis, leading to conclusions and recommendations for the most suitable deep learning approach model.


Paper Submission Last Date
31 - December - 2023

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