Even in a developed country like America the ratio of the number of hematologist and oncologist together to the number of people per doctor is 1:20,366. Then imagine how much it would be in the entire world? Medical industry has progressed tremendously over the past years and, specifically, the visual and image recognition is being used for many purposes, in many fields very actively. The general Artificial Intelligence (AI) topics, such as, Neural Networks (NN) and related concepts have also gained a lot of popularity lately. For this research, we have proposed a convolutional neural networks (CNN) architecture from scratch with data augmentation, image processing approaches, and neural network pattern recognition. We have compared the pre-existing architecture VGG-16 to our CNN model to determine if the CNN model was more suitable for these types of detection problems. Although, the data that we have used to train the model in our research is comparably small to any industrial application database, the proposed model displayed better accuracy and results than the VGG-16 for this kind of detection problem. Moreover, the proposed model uses less computational and memory power than the VGG-16 model. The secondary purpose of this research is to reduce the use of datasets that are from unrecognized sources. In this paper, we show how we can take the consent of the user and store the data to build a true data set for future educational and medical researchers and to retrain the models for better results.


Neural Network data augmentation image processing approaches


How to Cite
Sadeq Thamer Hlama. (2022). Types of Brain Tumor Detection Using Novel Deep Learning. Texas Journal of Engineering and Technology, 11, 63–72. Retrieved from https://zienjournals.com/index.php/tjet/article/view/2284


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