<![CDATA[Brain tumors are extremely dangerous to one's health. If unchecked cell proliferation is not identified and treated promptly, it can lead to mortality, raise intracranial pressure, and endanger lifespan. To remove the tumor and lengthen the patient's life, early illness identification and drug administration are essential. In this research paper, we aim to improve the effectiveness of magnetic resonance imaging (MRI) equipment to identify cancerous brain tumour cells. It helps experts identify diseases faster. We classify brain tumour cells based on an image set of 3264 images with effective classification models such as ResNet50, InceptionV3, VGG19, EfficientNetB7, DenseNet201, MobileNetV2, Xception, etc. Besides, we also proposed two combined models: pooling (Xception + ResNet50) and pooling (MobileNetV2 + EfficientNetB0) to evaluate the effectiveness and found that the pooling model (MobileNetV2 + EfficientNetB0) gives the highest result, with 100% for the training set, 98% for the valid set, and 78% for the test set. We continued to improve the model by randomly re-dividing the data set with a Train-Valid-Test ratio of 60:20:20 and obtained an increased F1-score of 97%. We continued to improve the model again using the data augmentation techniques to create a larger data set, and the results far exceeded expectations with an F1-score of almost 100% for all classes. Based on the results, we found that combining MobileNetV2 with EfficientNetB0 is suitable for detecting brain tumour cancer cells. Aids in the early detection of dangerous cancers before they spread and endanger human health.]]>
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