A Novel Inherited Modeling Structure of Automatic Brain Tumor Segmentation from MRI

Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to death.Magnetic resonance imaging methods capture skull images that contain healthy and affected tissue.Radiologists checked the affected tissue in the slice-by-slice manner,which was timeconsuming and hectic task.Therefore,auto segmentation of the affected part is needed to facilitate radiologists.Therefore,we have considered a hybrid model that inherits the convolutional neural network(CNN)properties to the support vector machine(SVM)for the auto-segmented brain tumor region.The CNN model is initially used to detect brain tumors,while SVM is integrated to segment the tumor region correctly.The proposed method was evaluated on a publicly available BraTS2020 dataset.The statistical parameters used in this work for the mathematical measures are precision,accuracy,specificity,sensitivity,and dice coefficient.Overall,our method achieved an accuracy value of 0.98,which is most prominent than existing techniques.Moreover,the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.

Block-Wise Neural Network for Brain Tumor Identification in Magnetic Resonance Images

The precise brain tumor diagnosis is critical and shows a vital role in the medical support for treating tumor patients.Manual brain tumor segmentation for cancer analysis from many Magnetic Resonance Images(MRIs)created in medical practice is a problematic and timewasting task for experts.As a result,there is a critical necessity for more accurate computeraided methods for early tumor detection.To remove this gap,we enhanced the computational power of a computer-aided system by proposing a finetuned Block-Wise Visual Geometry Group19(BW-VGG19)architecture.In this method,a pre-trained VGG19 is fine-tuned with CNN architecture in the block-wise mechanism to enhance the system`s accuracy.The publicly accessible Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)dataset collected from 2005 to 2020 from different hospitals in China has been used in this research.Our proposed method is simple and achieved an accuracy of 0.98%.We compare our technique results with the existing Convolutional Neural network(CNN),VGG16,and VGG19 approaches.The results indicate that our proposed technique outperforms the best results associated with the existing methods.

A Novel Hybrid Machine Learning Approach for Classification of Brain Tumor Images

Abnormal growth of brain tissues is the real cause of brain tumor.Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient.The manual segmentation of brain tumor magnetic resonance images(MRIs)takes time and results vary significantly in low-level features.To address this issue,we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network(CNN)for reliable images segmentation by considering the low-level features of MRI.In this model,we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN model.To handle the classification process,we have collected a total number of 2043 MRI patients of normal,benign,and malignant tumor.Three model CNN,multi-level CNN,and ResNet-50 based multi-level CNN have been used for detection and classification of brain tumors.All the model results are calculated in terms of various numerical values identified as precision(P),recall(R),accuracy(Acc)and f1-score(F1-S).The obtained average results are much better as compared to already existing methods.This modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis.