@article{Neha Bhagat, Dr. Gurmanik Kaur_2021, title={Improved Classification System of Brain Tumor from MRI Images Using Swarm Intelligence Based Convolution Neural Networks}, url={http://www.thedesignengineering.com/index.php/DE/article/view/1859}, abstractNote={<p><strong><em>&nbsp;</em></strong>Since human brain tumours are the most common form of tumour affecting both men and women, image processing-based medical diagnosis has seen a significant rise in recent years. It is, however, curable if it is detected and classified at an initial stage. Researchers have been working on a complex mechanism for classifying human brain tumours for a long time. The use of image processing to classify tumours from Magnetic Resonance Imaging (MRI) is common in the detection of brain disorders. So, utilizing Convolution Neural Network (CNN) as a deep learning, a swarm Intellect -Based Improved Brain Tumor Segmentation and Classification (I-BTSC)model is built from MRI images in this research work. First, we use the Swarm-based Grasshopper Optimization (SGO) algorithm to segment tumour regions using the conventional K-means concept. Following tumour region segmentation, feature extraction is used to determine the feature collection, and the fitness role is again served by SGO-based feature selection. Finally, CNN is used as a deep learning methodology to train the suggested I-BTSC system, which is then calculated. The suggested I-BTSC system is simulated using a publicly accessible “Contrast-Enhanced MRI Dataset”, and the outcomes show that the suggested model is more efficient than previous research work interms of performance measures.</p&gt;}, journal={Design Engineering}, author={Neha Bhagat, Dr. Gurmanik Kaur}, year={2021}, month={Jun.}, pages={1525- 1546} }