Image-classification for Brain Tumor using Pre-trained Convolutional Neural Network : Bildklassificering för hjärntumör medhjälp av förtränat konvolutionell tneuralt nätverk

Sammanfattning: Brain tumor is a disease characterized by uncontrolled growth of abnormal cells inthe brain. The brain is responsible for regulating the functions of all other organs,hence, any atypical growth of cells in the brain can have severe implications for itsfunctions. The number of global mortality in 2020 led by cancerous brains was estimatedat 251,329. However, early detection of brain cancer is critical for prompttreatment and improving patient’s quality of life as well as survival rates. Manualmedical image classification in diagnosing diseases has been shown to be extremelytime-consuming and labor-intensive. Convolutional Neural Networks (CNNs) hasproven to be a leading algorithm in image classification outperforming humans. Thispaper compares five CNN architectures namely: VGG-16, VGG-19, AlexNet, EffecientNetB7,and ResNet-50 in terms of performance and accuracy using transferlearning. In addition, the authors discussed in this paper the economic impact ofCNN, as an AI approach, on the healthcare sector. The models’ performance isdemonstrated using functions for loss and accuracy rates as well as using the confusionmatrix. The conducted experiment resulted in VGG-19 achieving best performancewith 97% accuracy, while EffecientNetB7 achieved worst performance with93% accuracy.