Brain Tumor Detection using CNN and VGG-16 Model
Keywords:
Brain Tumor, CNN, VGG16, Alex Net, RF, SVM, KNNAbstract
Modern healthcare heavily relies on the timely and precise detection of brain tumors (BT), a critical factor influencing patient outcomes. Uncontrolled proliferation of brain cells can lead to the growth of BT. Common imaging methods, such as MRIs, CT scans, and radiography, are routinely employed to detect and diagnose these tumors. Despite these advanced techniques, the automatic identification of brain tumors in their early stages remains challenging, particularly through MRI scans. While reviewing the conventional approaches in brain tumor identification, several persistent issues were identified. These include a lack of diversity in the images, challenges in feature extraction, and a limited evaluation of extensive multiclass datasets. This study investigates the efficacy of various state-of-the-art ML and DL models, including RF, SVM, KNN, CNN, VGG16, and AlexNet, for brain tumor identification. Python was executed for simulating these models, and their performance is assessed on two extensive databases— BTIS and Glioma. Evaluation is conducted based on three crucial metrics: accuracy, precision, and recall. For maximizing the accuracy of tumor boundary definition and overall algorithmic efficacy, the SS algorithm is introduced. In this study, the CNN algorithm serves as a benchmark for the analysis and to categorize tumorous images, leveraging the efficiency of VGG16 and AlexNet in capturing intricate image features. The outcomes indicate the reliability of these algorithms in accurately diagnosing and localizing brain tumors. The comparative study systematically assesses and illustrates the effectiveness and limitations of these algorithms. Notably, VGG16 demonstrated impressive results with an accuracy of 95.23%, recall of 93.72%, precision of 93.27%, and an F1-Score of 94.22%.
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