Abstract
Meningiomas are the most prevalent benign intracranial life-threatening brain tumors, with a life expectancy of a few months in the later stages, so this type of tumor in the brain image should be recognized and detected efficiently. The source of meningiomas is unknown. Radiation exposure, particularly during childhood, is the sole recognized environmental risk factor for meningiomas. The imaging technique of magnetic resonance imaging (MRI) is commonly used to detect most tumor forms as it is a non-invasive and painless method. This study introduces a CNN-HHO integrated automated identification model, which makes use of SeaLion optimization methods for improving overall network optimization. In addition to these techniques, various CNN models such as Resnet, VGG, and DenseNet have been utilized to give an overall influence of CNN with SeaLion in each methodology. Each model is tested on our benchmark dataset for accuracy, specificity, dice coefficient, MCC, and sensitivity, with DenseNet outperforming the other models with a precision of 98%. The proposed methods outperform existing alternatives in the detection of brain tumors, according to the existing experimental findings.
Highlights
The brain tumor and its analysis are of tremendous interest because of the growing innovations in medical image processing
The comparison is based on a set of 50 magnetic resonance imaging (MRI) scans, which are divided into two groups: normal and abnormal
Brain tumor cases are increasing, and this has brought about a certain increase in strain on medical staff in this area
Summary
The brain tumor and its analysis are of tremendous interest because of the growing innovations in medical image processing. According to the National Brain Tumor Foundation’s (NBTF) global review, the improvement of brain tumor diagnoses among patients and the death rate due to brain tumors are outpacing earlier years’ findings clinical experts can give patients more effective e-health care services, thanks to developments in medical imaging for this enhancement [1]. E-health care systems have a wide range of applications in medicine [2] Due to their high accuracy and efficient results as presented by the radiologist, computervision-based biomedical imaging systems have gained appeal among clinical specialists, allowing them to handle treatment-related concerns more efficiently. Magnetic resonance imaging (MRI) is well-known medical equipment that can be used to diagnose and study a variety of disorders, including brain tumors, neurological ailments, and epilepsy, among others. Machine learning ( deep learning) improvements have made it easier to discover, classify, and measure trends in medical images
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