Computer-Aided Early Melanoma Brain-Tumor Detection Using Deep-Learning Approach.
Rimsha AsadSaif Ur RehmanAzhar ImranJianqiang LiAbdullah AlmuhaimeedAbdulkareeem AlzahraniPublished in: Biomedicines (2023)
Brain tumors affect the normal functioning of the brain and if not treated in time these cancerous cells may affect the other tissues, blood vessels, and nerves surrounding these cells. Today, a large population worldwide is affected by the precarious disease of the brain tumor. Healthy tissues of the brain are suspected to be damaged because of tumors that become the most significant reason for a large number of deaths nowadays. Therefore, their early detection is necessary to prevent patients from unfortunate mishaps resulting in loss of lives. The manual detection of brain tumors is a challenging task due to discrepancies in appearance in terms of shape, size, nucleus, etc. As a result, an automatic system is required for the early detection of brain tumors. In this paper, the detection of tumors in brain cells is carried out using a deep convolutional neural network with stochastic gradient descent (SGD) optimization algorithm. The multi-classification of brain tumors is performed using the ResNet-50 model and evaluated on the public Kaggle brain-tumor dataset. The method achieved 99.82% and 99.5% training and testing accuracy, respectively. The experimental result indicates that the proposed model outperformed baseline methods, and provides a compelling reason to be applied to other diseases.
Keyphrases
- deep learning
- induced apoptosis
- convolutional neural network
- cell cycle arrest
- machine learning
- white matter
- gene expression
- resting state
- newly diagnosed
- loop mediated isothermal amplification
- healthcare
- endoplasmic reticulum stress
- end stage renal disease
- pulmonary embolism
- oxidative stress
- ejection fraction
- prognostic factors
- emergency department
- cerebral ischemia
- signaling pathway
- chronic kidney disease
- subarachnoid hemorrhage
- brain injury
- neural network