- Elias Hossain 1
Due to difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from 3D MRI and CT scans utilizing 3D UNet Design and ResNet50, taken after by conventional classification strategies. In this inquire, the ResNet50 picked up exactness with 98.96%, and the 3D UNet scored 97.99% precision among the different state-of-the-art methods of Deep Learning. In expansion, the image fusion approach combines the multimodal images and makes a fused image to extricate more highlights from the medical images. Other than that, we have identified the loss function by utilizing several dice measurements approach and received Dice Result on top of a specific test case. On the other hand, a software integration pipeline was integrated to deploy the concentrated model into the webserver for accessing it from the software system using the Representational state transfer (REST) API. Eventually, the suggested models were validated through the AUC – ROC curve and Confusion Matrix and compared with the existing research articles to understand the underlying problem. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through the various imaging modalities.
E. Hossain, M. S. Hossain, M. S. Hossain, S. A. Jannat, M. Huda et al., "Brain tumor auto-segmentation on multimodal imaging modalities using deep neural network," Computers, Materials & Continua, vol. 72, no.3, pp. 4509–4523, 2022.