首页|Accurate brain tumor region segmentation using local intensity deviation based ResNet50
Accurate brain tumor region segmentation using local intensity deviation based ResNet50
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NETL
NSTL
Abstract At present, brain tumors are the world’s most deadly disease. The brain may be impacted by tumors that cause harm to healthy brain tissue or increase intracranial pressure. Therefore, tumor cell’s rapid growth may be fatal. In the realm of medical image analysis, brain tumor identification is a crucial challenge since timely and precise diagnosis is essential for treatment planning and patient care. Automating the diagnosis of brain tumors using medical images, such as MRI scans, has shown considerable potential thanks to deep learning algorithms. Image segmentation is required for the diagnosis of brain malignancies. Tumor detection involves complex stages that need the identification of two distinct locations in brain tumor images. In this work, we propose a method for brain tumor detection using a combination of image preprocessing techniques and the ResNet50 deep learning model. MRI images are first preprocessed by normalizing pixel intensities, resizing for uniformity, sharpening edges to highlight tumor boundaries, and enhancing abnormal areas through intensity deviation. The images are then divided into smaller slices for more focused analysis. These processed images are used to train and test the ResNet50 model, leading to improved accuracy in identifying brain tumors. In this work, we achieved an accuracy of 99.25%, sensitivity of 99.36%, specificity of 98.78%, precision of 99.12%, F1 score of 99.05%, and recall of 99.79%. Compared to existing approaches, it can identify the tumor more precisely and with less processing time.
Kadiyala Vijaya Kumar、Dr D Mabuni
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Dravidian University||G Pullaiah College of Engineering and Technology
NETL
NSTL
