02100nas a2200241   4500000000100000000000100001008004100002260001200043653001000055653002400065653003300089653002500122653002700147100002800174700003100202700001500233245012300248856007900371300001000450490000600460520137800466022001401844       2022                            d  c12/202210aTumor10aDeep Belief Network10aLocal Neighborhood Structure10aScattering Transform10aTumor Characterization1 aMaahi Amit Khemchandani1 aShivajirao Manikrao Jadhav1 aB. R. Iyer00aBrain Tumor Segmentation and Identification Using Particle Imperialist Deep Convolutional Neural Network in MRI Images  uhttps://www.ijimai.org/journal/sites/default/files/2022-11/ijimai7_7_4.pdf  a38-470 v73 aFor the past few years, segmentation for medical applications using Magnetic Resonance (MR) images is concentrated. Segmentation of Brain tumors using MRIpaves an effective platform to plan the treatment and diagnosis of tumors. Thus, segmentation is necessary to be improved, for a novel framework. The Particle Imperialist Deep Convolutional Neural Network (PI-Deep CNN) suggested framework is intended to address the problems with segmenting and categorizing the brain tumor. Using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm, the input MRI brain image is segmented, and then features are extracted using the Scatter Local Neighborhood Structure (SLNS) descriptor. Combining the scattering transform and the Local Neighborhood Structure (LNS) descriptor yields the proposed descriptor. A suggested Particle Imperialist algorithm-trained Deep CNN is then used to achieve the tumor-level classification. Different levels of the tumor are classified by the classifier, including Normal without tumor, Abnormal, Malignant tumor, and Non-malignant tumor. The cell is identified as a tumor cell and is subjected to additional diagnostics, with the exception of the normal cells that are tumor-free. The proposed method obtained a maximum accuracy of 0.965 during the experimentation utilizing the BRATS database and performance measures.  a1989-1660