Abstract
Brain tumors are a life-threatening disease, and a lot of people are losing their lives. These brain tumors are abnormal cells that develop in and around the brain. This research explores the cutting edge of medical imaging processing, focusing on enhancing the detection and categorization of brain tumors. EfficientNetB0 is the most advanced deep learning architecture that has been thoroughly compared with other deep learning models in order to improve brain tumor classification accuracy using the Kaggle MRI image dataset with 7023 images. The drawbacks of manual tumor identification techniques are discussed, and precise classification using deep neural networks is proposed, with special attention to the transition from binary to multiclassification. This chapter’s primary focus is on improving and optimizing the EfficientNetB0 model through the addition of trainable layers on top of its basic architecture. Several techniques are used like global average pooling for spatial and dimensionality reduction with reduced parameters, dropout to drop layers, and dense net with softmax for multiclass classification. Concurrently, strategic layer freezing is used to refine the deep learning models for foundation design. The results show that the finetuned EfficientNetB0 model with hyper-parameter optimization guarantees exceptional brain tumor accuracy. EfficientNetB0 has achieved a good accuracy of 99.7% and a precision of 99.5% compared to Resnet50, VGG16, InceptionV3 and Xception. This work presents a unique deep-learning method in accordance with a transfer learning strategy for assessing brain cancer categorization accuracy using the enhanced ResNet50 model. As we advance the state-of-the-art, this chapter offers researchers, medical professionals, and patients a solid foundation for accurate and timely brain tumor diagnoses, thus contributing to the research community.
