![]() This work obtains a comprised feature set by retrieving the diverse deep features from the ResNet50 deep learning model and feeds them as input to the classifier. The proposed method can retain the optimal computational efficiency with high-dimensional deep features. The proposed model uses the ResNet50 model with a modified layer architecture including five convolutional layers and three fully connected layers. This paper explains the modeling of a novel technique by integrating the modified ResNet50 with the Enhanced Watershed Segmentation (EWS) algorithm for brain tumor classification and deep feature extraction. ![]() ![]() Different from traditional machine learning methods that are just targeting to enhance classification efficiency, this work highlights the process to extract several deep features to diagnose brain tumor effectively. Nowadays, researchers need to attain a more effective, accurate, and trustworthy brain tumor tissue detection and classification approach. Cancer diseases, primarily the brain tumor, have been exponentially raised which has alarmed researchers from academia and industry. It also involves stochastic approaches to help in developing enhanced watershed modeling. This work delivers a novel technique to detect brain tumor with the help of enhanced watershed modeling integrated with a modified ResNet50 architecture.
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