Brain Tumor Detection by using Fine-tuned MobileNetV2 Deep Learning Model
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Abstract
Most of the deaths in the world happen due to Cancer. It is a disease in which the cells of our body organs or tissues grow in an undisciplined way which in turn can harm our normal cells and tissues in our body. These cells very smartly trick the immune system so that the cancerous cells are kept alive and are not destroyed. In the human body, tumors can be classified into three types: cancerous, non-cancerous, and pre-cancerous. Timely identification of the cancer can be helpful in many ways. As it improves a patient’s chances of survival. The most valuable, uncomplicated technique used is MRI scans for predicting tumor is a tough task and have chances of human error. So to be more accurate with our predictions we have moved on to use computerized techniques to ease the work. The focus of this research is the development of an automated brain tumor classification system using magnetic resonance imaging (MRI) scans, leveraging a deep learning model. The proposed model employs a convolutional neural network (CNN) architecture known as MobileNetV2, which is trained on a pre-processed MRI image dataset to classify brain tumors into one of two categories: tumor tissues and normal brain tissue. To mitigate overfitting and expand the dataset, data augmentation techniques are employed. The trained model achieves high accuracy, sensitivity, and specificity in classifying brain tumors. Proposed CNN model outperformed other deep learning models, including VGG16, Xception, and ResNet50, which were used for comparison.
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