HOG transformation based feature extraction framework in modified Resnet50 model for brain tumor detection.

• In this research work we have used the HOG transformation which is related to transforming the shape of an object, calculating the gradient in 'localized' portions and generating the HOG based histograms for each of these regions separately. • Then, it is merged with Modified ResNet50 model to detect brain tumor in MRI images. • The computational complexity of the proposed method is relatively lower with higher accuracy as compared to the existing methods. • This is due to the fact that the proposed model is based on an appropriate classification algorithm and due to HOG transformation the feature selection has been very effective for the detection of brain tumor. Brain tumor happens due to the instant and uncontrolled cell growth. It may lead to death if not cured at an early stage. In spite of several promising results and substantial efforts in this research area, the real challenge is to provide the accurate classification and segmentation. The key issue in brain tumor detection develops from the irregular changes in the tumor size, shape and location. In assessing the MRI images, computer-aided diagnoses are playing an extraordinary role and can help clinicians/radiologist. Nowadays, brain tumor has become the most incursive ailment that leads to a very short life expectancy when it reaches its highest grade. This research paper has created a new model using histogram of gradient (HOG) based neural features from MRI images for tumors detection. This research has conducted the feature optimization approach to achieve additional instinctive features from the complex feature vector. We developed a Modified ResNet50 model with HOG technique. The modified ResNet50 model can accurately extract the deep feature using deep learning approach. This model is applied along with the upgraded layered architecture in order to keep the optimal computational efficiency. We have also used the augmentation and feature extraction techniques using machine learning-based ensemble classifier that further provides the optimized fusion vector to identify the tumor. Such hybrid approach provides excellent performance with the detection accuracy of 88% with HOG and modified ResNet50. The results are also compared with the recent state of art methods. [ABSTRACT FROM AUTHOR]