Analysis of Brain MRI Images Using Improved CornerNet Approach

Abstract

The brain tumor is a deadly disease that is caused by the abnormal growth of brain cells, which affects the human blood cells and nerves. Timely and precise detection of brain tumors is an important task to avoid complex and painful treatment procedures, as it can assist doctors in surgical planning. Manual brain tumor detection is a time-consuming activity and highly dependent on the availability of area experts. Therefore, it is a need of the hour to design accurate automated systems for the detection and classification of various types of brain tumors. However, the exact localization and categorization of brain tumors is a challenging job due to extensive variations in their size, position, and structure. To deal with the challenges, we have presented a novel approach, namely, DenseNet-41-based CornerNet framework. The proposed solution comprises three steps. Initially, we develop annotations to locate the exact region of interest. In the second step, a custom CornerNet with DenseNet-41 as a base network is introduced to extract the deep features from the suspected samples. In the last step, the one-stage detector CornerNet is employed to locate and classify several brain tumors. To evaluate the proposed method, we have utilized two databases, namely, the Figshare and Brain MRI datasets, and attained an average accuracy of 98.8% and 98.5%, respectively. Both qualitative and quantitative analysis show that our approach is more proficient and consistent with detecting and classifying various types of brain tumors than other latest techniques.

Feasibility of Synthetic Computed Tomography Images Generated from Magnetic Resonance Imaging Scans Using Various Deep Learning Methods in the Planning of Radiation Therapy for Prostate Cancer

“…Since the quality of sCTs depends on that of the original MRI, adequate MRI sequences for sCT generation need to be established. Alternatively, it is worth considering the use of a combination of multiple MRI sequences that complement sCT generation [ 15 , 16 ].…”

“…Early investigations regarding sCT generation for RT simulation using deep learning techniques have focused on RT for cranial sites wherein inter-personal or temporal anatomical variations are limited. In addition, because the technique for the extraction of image features from MRI, cranial MRI has been favored due to the advances of those techniques in the brain MRI using various neural networks [ 16 , 17 ]. However, the use of sCTs for RT was expanded to other anatomic sites including the chest, abdomen, and pelvis [ 14 , 15 , 18 , 19 ].…”

Improved Text Summarization of News Articles Using GA-HC and PSO-HC

“…Moreover, multiple aspects including readability, correctness, completeness, and compactness of documents can be considered to improve the quality of summary. Moreover, the deep learning models will be considered for the data extraction and optimized using metaheuristic techniques [56][57][58][59][60][61][62].…”

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

“…Recently, deep learning showed a huge improvement for cell segmentation [ 27 ], skin melanoma detection [ 28 ], hemorrhage detection [ 29 ], and a few more [ 30 , 31 ]. In medical imaging, deep learning was successful, especially for breast cancer [ 32 ], COVID-19 [ 33 ], Alzheimer’s disease recognition [ 34 ], brain tumor [ 35 ] diagnostics, and more [ 36 , 37 , 38 ]. CNN is a type of deep learning that includes several hierarchies of layers.…”